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Context: Summary33315.7SummaryThethemeofthischapterwasautomaticprogramanalysis:howtoobtainin-formationabouttheexecutionofaprogram,withoutactuallyexecutingit.Wepresentedabstractinterpretation:asystematictechniqueforprogramanalysis,andreconsideredtheScheme0binding-timeanalysis(fromSection5.2)asanab-stractinterpretation.Wepresentedaclosureanalysis,whichproducesinformationaboutfunctionapplicationsinhigher-orderlanguages,andusedthistoextendthe(cid:12)rst-orderScheme0binding-timeanalysistoahigher-orderScheme1binding-timeanalysis.Finally,wepresentedLaunchbury’sprojection-basedbinding-timeanalysisforpartiallystaticdata.15.8ExercisesExercise15.1Constructaprogramanalysisthatdetectsduplicableanddiscard-ablevariables(theconceptofduplicabilityanddiscardabilitycanbeusedtocontrolunfolding,seeChapter5).2Exercise15.2Modifythebinding-timeanalysispresentedinSection5.2tocom-putepolyvariantdivisions.Indicatethenecessarychanges(ifany)tothedomains,theanalysisfunctionsBeandBv,thecongruencerequirement,thestrategyfor(cid:12)ndingthebestdivision,etc.2Exercise15.3Considertheprogram(define(fxs)(sum(map(lambda(x)(+x1))xs)))(define(sumys)(if(null?ys)0(+(carys)(sum(cdrys)))))(define(mapgzs)(if(null?zs)’()(cons(g(carzs))(mapg(cdrzs)))))1.Doclosureanalysisandbinding-timeanalysisofthefunctionf,assumingthatxsisstatic.2.Nowassumetheprogramalsocontainsthefunctionde(cid:12)nition(define(hws)(map(lambda(w)w)ws))wherewsisdynamic.Redotheclosureanalysisandthebinding-timeanalysisoffandcommentontheresults.2
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Context: 118Chapter3  (cid:127)  Working with Snort Rulesincluded file into the main configuration file at the point where it is included. In fact,most of the predefined rules that come with the Snort distribution are found in includefiles. All files in the Snort distribution whose name ends with .rules contain rulesand they are included in the snort.conf file. These rule files are included in themain snort.conf file using the “include” keyword. The following is an example ofincluding myrules.rules file in the main configuration file.include myrules.rulesIt is not necessary that the name of the rules file must end with .rule. You canuse a name of your choice for your rule file.3.7.8Sample snort.conf FileThe following is a sample configuration file for Snort. All lines starting with the #character are comment lines. Whenever you modify the configuration file, you have torestart Snort for the changes to take effect.# Variable Definitionsvar HOME_NET 192.168.1.0/24var EXTERNAL_NET anyvar HTTP_SERVERS $HOME_NETvar DNS_SERVERS $HOME_NETvar RULE_PATH ./# preprocessorspreprocessor frag2preprocessor stream4: detect_scanspreprocessor stream4_reassemblepreprocessor http_decode: 80 -unicode -cginullpreprocessor unidecode: 80 -unicode -cginullpreprocessor bo: -nobrutepreprocessor telnet_decodepreprocessor portscan: $HOME_NET 4 3 portscan.logpreprocessor arpspoof# output modulesoutput alert_syslog: LOG_AUTH LOG_ALERToutput log_tcpdump: snort.logoutput database: log, mysql, user=rr password=boota \   dbname=snort host=localhostoutput xml: log, file=/var/log/snortxml# Rules and include filesinclude $RULE_PATH/bad-traffic.rulesinclude $RULE_PATH/exploit.rules
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Context: The Snort Configuration File117There may be additional steps to make the output module work properly. In thecase of MySQL database, you need to setup a database, create tables, create user, setpermissions and so on. More information on configuring output modules is found inChapter 4.3.7.5Defining New Action TypesYou already know that the first part of each Snort rule is the action item. Snort haspredefined action types; however, you can also define your own action types in the con-figuration file. A new action type may use multiple output modules. The followingaction type creates alert messages that are logged into the database as well as in a file inthe tcpdump format.ruletype dump_database{  type alert  output database: alert, mysql, user=rr dbname=snort \    host=localhost  output log_tcpdump: tcpdump_log_file}This new action type can be used in rules just like other action types. dump_database icmp any any -> 192.168.1.0/24 any \  (fragbits: D; msg: "Don’t Fragment bit set";)When a packet matches the criteria in this rule, the alert will be logged to the data-base as well as to the tcpdump_log_file.3.7.6Rules ConfigurationThe rules configuration is usually the last part of the configuration file. You cancreate as many rules as you like using variables already defined in the configuration file.All of the previous discussion in this chapter was about writing new rules. The rulesconfiguration is the place in the configuration file where you can put your rules. How-ever the convention is to put all Snort rules in different text files. You can include thesetext files in the snort.conf file using the “include” keyword. Snort comes withmany predefined rule files. The names of these rule files end with .rule.  You havealready seen in the last chapter how to put these rule files in the proper place during theinstallation process.3.7.7Include FilesYou can include other files inside the main configuration file using the includekeyword. You can think of including a file as equivalent to inserting the contents of the
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Context: Summary611.Changinglanguagestyle,forexamplefromnon-linear,value-orientedexpres-sionstolinear,command-orientedmachinecode.2.Sequentializingnaturallynon-sequentialprocesses,e.g.devisingastacktoremembertemporaryresultsobtainedwhileevaluatingthepartsofanarith-meticexpressioninaparticularsequence.3.Loweringthelevelofabstractionfromsourcelevel(e.g.‘higher-levellan-guage’)toatargetcodeleveltailoredtothatparticularsourcecode,e.g.P-codeforimplementingPascal.4.Devisingdatastructuresatthemachinecodelevelsuitableforimplement-inghigher-leveldata(products,sums,recursivelyde(cid:12)neddata,functionsasvalues,etc.),allimplementedinalinearstoragespace.5.Implementingvaluemanagement,e.g.goingfromimplicitlast-in(cid:12)rst-outscoperulestostackedactivationblockscontainingenvironmentbindings.3.6SummaryThischapterconcernedinterpreters:operationalsemanticsforprogramminglan-guagesinadirectlyexecutableform.Importanttopicsincludedthefollowing:(cid:15)interpretedprogramsusuallyrunslowerthandirectlyexecutedones;(cid:15)thespeeddi(cid:11)erenceisoftenapracticallysigni(cid:12)cantfactor;(cid:15)theinterpretationoverheadisnearlyconstantforagivensourceprogrambeinginterpreted|theconstantdependsontheprogrambutnottheinput;(cid:15)thelambdacalculusisausefulnotationforde(cid:12)ningfunctions;(cid:15)computationinthelambdacalculuscanbede(cid:12)nedbyreductionrelations;(cid:15)evaluationstrategiesusedinfunctionallanguagesarerestrictionsoftheserelations;(cid:15)call-by-nameevaluationterminatesmoreoftenthancall-by-value.WealsopresentedinterpreterswritteninMLforthreemini-languages:thecall-by-valuelambdacalculus,(cid:12)rst-orderrecursionequations,andasimpleimperative(cid:13)owchartlanguage.Finally,wesummarizedtheachievementsandnon-achievementsofcompilationbyautomaticprogramspecialization.
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Context: gramtextsillustratingalloftheseareincluded.Theimportanttopicofbinding-timeanalysisisintroduced,andavarietyoftechnicalproblemsareidenti(cid:12)ed,analysed,andsolved.Chapter5describesaself-applicablepartialevaluatorfora(cid:12)rst-orderlanguageofrecursiveequations.ManyoftheprinciplesofChapter4canbeadaptedtothisstrongerprogramminglanguage.Chapter6presentsonewaytorecognizeagoodpartialevaluator,andshows
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Context: edtokeeptrackofwhichsourceprogramfragmentshavetobecompiled.Aftercompilinganif-statement,compilationhastogoonfromtwodi(cid:11)erentpoints.One(tobeexecutedonafalsecondition)ischaracterizedbyQtail,theother(tobeexecutedwhenajumpismade)ischaracterizedbylbl,thetargetoftheconditionaljump.Thereforethetwotuples(cont,Qtail)and(jump,lbl)areaddedtopendingprovidedtheyarenotalreadythereandthattheyhavenotalreadybeenprocessed(thatis,theyarenotinmarked).Onepointneedsfurtherexplanation:thepairs(init,Q),(cont,Qtail),and(jump,lbl)areclaimedtobeofform(pp,vs).Thisdoesnotseemreasonableat(cid:12)rstsightsincevsshouldcontainthevaluesofalloftheinterpreter’sstatic
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Context: Overviewofthebook171.7OverviewofthebookPrerequisitesOurpresentationstyleissemiformal.Ontheonehand,thevarioustermsandalgorithmsusedarepreciselyde(cid:12)ned.Forexample,theprogramswepresentmaybeunambiguouslyexecutedbyhand.Ontheotherhand,wedonotuseadvancedmathematicalconceptsandterminology(domains,algebras,categories,etc.);or-dinarydiscretemathematicsissu(cid:14)cient.WeassumethereadertobefamiliarwithaPascal-likeprogramminglanguage.PriorknowledgeofafunctionallanguagesuchasLisp,Scheme,ML,Miranda,orHaskellwouldmakesomepartseasiertofollow,butisnotaprerequisite.Fi-nally,someexperiencewithcompilers(e.g.anundergraduatecompilerconstructioncourse)wouldbedesirable.OutlinePartIintroducesconceptsandnotationofprogramminglanguages.Chapter2introducesfunctions,recursion,anddatatypes,andthedistinctionbetweenaprogram(text)andthefunctionitde(cid:12)nes.Chapter3de(cid:12)nestheconceptsofinterpreterandcompileranddiscussespro-gramrunningtimesandinterpretationoverhead.Thenthreemini-languagesarepresented:thelambdacalculus,(cid:12)rst-orderrecursionequations,anda(cid:13)owchartlanguage.Interpretersforexecutingthemaregivenalso,tointroducetheconceptsofabstractsyntax,environment,andclosure,whichwillbeusedinthepartialevaluatorspresentedlater.PartIIpresentspartialevaluatorsfortwoofthemini-languagesintroducedinChapter3.Thisintroducesavarietyoftechniquesforpartialevaluation,usefulalsointhepartialevaluationofstrongerlanguages.Chapter4concernsthe(cid:13)owchartlanguage.Apartialevaluatorisdevelopedinconsiderabledetail,emphasizingconcreteexamplesandcarefullymotivatingthevariousdesigndecisionsthataretaken.Enoughdetailsaregiventoallowthereadertoimplementthepartialevaluatorandgeneratecompilersonhisorherowncomputer.Itisshownbyexamplesthatpartialevaluationcancompile,generatecompilers,andevengenerateacompilergenerator.Thekeytothelattertwoisself-applicationasintheFutamuraprojectionsofSection1.5.Itmaycomeasasurprisethatself-applicationleadstoconsiderableimprovementsincompilerrunningtimes.Programtextsillustratin
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Context: moreinformative,sinceitsays:onlytheselambdasarepossible.Abstractinterpretationofaprogramproducesadescription((cid:30),(cid:26))2ResEnv(cid:2)VarEnv.RecallthatResEnvandVarEnvaremappings(fromfunctionandvariablenames)tolabelsets,sothesetResEnv(cid:2)VarEnvofdescriptionscanbeordered
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Context: 360ProgramTransformationLazycontexts.These,asde(cid:12)nedbefore,canonlytakeonthefollowingforms.Foruseintransformationweaddaclausefortermswithfreevariables.1.g1(g2...(gn[ht1...tm]tn1...tnmn)...)t11...t1m1(n(cid:21)0),or2.g1(g2...[gn(ct1...tm)tn1...tnmn]...)t11...t1m1(n(cid:21)1),or3.g1(g2...[gnvtn1...tnmn])...)t11...t1m1(n(cid:21)0)Explanations.Acallht1...tncanberewrittenatonce,whileacallgt0t1...tnforcesevaluationoft0tobringitsoutermostconstructor‘tothesurface’beforerewritingcanbedone.The‘attentionpoint’canthusappearinsideanestofgcalls,asseeninallthreecases.Forprogramtransformation,cases1and2canbeunfoldedatonce.Case3cannot,butonemayde(cid:12)neanewfunctionwith3asitsrightside,andinstantiatevtoallowthegncalltoberewritten.Shortform.Wewritee[ft0t1...tn]toindicateallthreecontextforms,soe[]=g1(g2...[]...)...)...).17.4.1AtransformationalgorithmBythefollowingstrategyintermediatedatastructuresproducedinonepartofaprogramandconsumedinanotherpartmaybeeliminatedentirely.Thispatternwasseenin‘doubleappend’ofSection17.2.1,whichillustratescombiningphasesofamultipassalgorithm.Inotherwords,thedisciplineisgoodforsymbolicfunctioncomposition.Theprocessisautomaticandpreserveslazysemantics,butinits(cid:12)rstversionwillnotterminateonallprograms.Afterillustratinghow(andwhy)itworks,wediscusswaystoguaranteetermination.DeforestationRulesetsPendingandOutareusedinFigure17.5asinthepartialevaluationalgorithm.Wenowarguethatthealgorithmpreservesthelazysemantics,withthefollowingassumptions:1.Global:thatanypartoftheprogram’soutputonanyinputcanpossiblybedemanded;and2.Local:thatevaluationproceedsonlyuntilavalue’soutermostconstructorisknown.Theglobalassumptionsensurethetransformedprogramwillcoverallpossiblerun-timesituations.Theprogramisprocessedsystematically,generatingnewrulesto
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Context: 126Chapter3  (cid:127)  Working with Snort Rulesimap.rulesinfo.ruleslocal.rulesMakefileMakefile.amMakefile.inmisc.rulesmultimedia.rulesmysql.rulesnetbios.rulesnntp.rulesoracle.rulesother-ids.rulesp2p.rulespolicy.rulespop3.rulesporn.rulesrpc.rulesrservices.rulesscan.rulesshellcode.rulessmtp.rulessnmp.rulessql.rulestelnet.rulestftp.rulesvirus.rulesweb-attacks.rulesweb-cgi.rulesweb-client.rulesweb-coldfusion.rulesweb-frontpage.rulesweb-iis.rulesweb-misc.rulesweb-php.rulesx11.rulesFor example, all rules related to X-Windows attacks are combined inx11.rules file.# (C) Copyright 2001,2002, Martin Roesch, Brian Caswell, et al.#    All rights reserved.# $Id: x11.rules,v 1.12 2002/08/18 20:28:43 cazz Exp $#----------# X11 RULES#----------
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Context: 10PageFrameAllocation61ManagingAvailableMemory.......................................61HowMuchMemoryisThere?....................................61ManagingAvailableMemory.....................................63HowCanWeAccessaPageFrame?...................................63AKernelHeap...............................................63Furtherreading...............................................6311UserMode65SegmentsforUserMode..........................................65SettingUpForUserMode.........................................65EnteringUserMode............................................66UsingCforUserModePrograms.....................................67ACLibrary..............................................68FurtherReading..............................................6812FileSystems69WhyaFileSystem?............................................69ASimpleRead-OnlyFileSystem.....................................69InodesandWritableFileSystems.....................................70AVirtualFileSystem...........................................70FurtherReading..............................................7013SystemCalls71DesigningSystemCalls..........................................71ImplementingSystemCalls........................................71FurtherReading..............................................7214Multitasking73CreatingNewProcesses..........................................73CooperativeSchedulingwithYielding..................................73PreemptiveSchedulingwithInterrupts..................................74ProgrammableIntervalTimer....................................74SeparateKernelStacksforProcesses................................74DifficultieswithPreemptiveScheduling..............................75FurtherReading..............................................75References776
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Context: Overviewofthebook19cessingprograms:interpreters,compilers,andpartialevaluators.Chapter17explainstherelationbetweenpartialevaluationandclassicalprogramtransformationmethodssuchasBurstallandDarlington’sfold/unfoldtechnique.Finally,Chapter18givesanoverviewoftheliteratureonpartialevaluationandcloselyrelatedtopics,andservesasaguidetofurtherstudiesinthe(cid:12)eld.
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Context: 152Chapter4  (cid:127)  Plugins, Preprocessors and Output ModulesThe output file generated after using this line in snort.conf file is somethinglike the following:07/23-18:24:03.388106 ,ICMP Packet with TTL=100,ICMP,192.168.1.100,,192.168.1.2,,0:2:3F:33:C6:98,0:E0:29:89:28:59,0x4A,,,,,,100,0,51367,60,20,8,0,,07/23-18:25:51.608106 ,GET matched,TCP,192.168.1.2,1060,192.168.10.193,,0:E0:29:89:28:59,0:6:25:5B:29:ED,0x189,***AP***,0x55BCF404,0x8CBF42DD,,0x16D0,64,0,35580,379,20,,,,07/23-18:25:52.008106 ,GET matched,TCP,192.168.1.2,1061,192.168.10.193,,0:E0:29:89:28:59,0:6:25:5B:29:ED,0x1D0,***AP***,0x55628967,0x8D33FB74,,0x16D0,64,0,63049,450,20,,,,07/23-18:25:52.478106 ,GET matched,TCP,192.168.1.2,1061,192.168.10.193,,0:E0:29:89:28:59,0:6:25:5B:29:ED,0x1D0,***AP***,0x55628B01,0x8D33FC1B,,0x1920,64,0,63051,450,20,,,,07/23-18:25:52.708106 ,GET matched,TCP,192.168.1.2,1061,192.168.10.193,,0:E0:29:89:28:59,0:6:25:5B:29:ED,0x1EF,***AP***,0x55628C9B,0x8D33FCC1,,0x1D50,64,0,63053,481,20,,,,Each line in the output consists of fields as listed in Table 4-3.Table4-3 CSV OptionsNameDescriptionTimestampTime stamp including date and time.MsgMessage which is taken from the msg option of the rule.ProtoProtocol.SrcSource IP address.SrcportSource port number. No port number is present in  ICMP packets.DstDestination IP address.DstportDestination port.ethsrcSource Ethernet address.ethdstDestination Ethernet address.ethlenLength of Ethernet frame.tcpflagsIf the protocol is TCP, this part contains TCP flags.tcpseqTCP sequence number in  TCP packets.tcpackTCP acknowledgement number.
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Context: BTAbysolvingconstraints177((cid:21)x.x@y)@zand((cid:21)x.x@y)z.OurstrategyistotranslatetheinferencerulesofthesystemTypeCombintoconstraintsonthetypevariables(cid:11)associatedwiththesubexpressionsandboundvariablesofagivenetobeanalysed.Thenextstepisto(cid:12)ndthemostgeneralsubstitutionoftypetermsfortypevariablessuchthatallconstraintsaresatis(cid:12)ed.Thissubstitutioncharacterizesallpossiblecompletionsofe,andamongthesewechooseaminimalcompletion.Wede(cid:12)ne(cid:20)band(cid:20)ftobethe‘(cid:13)at’partialordersontypetermswhoseonlystrictinequalitiesareS<bDD!D<fDNotethat(t2!t1=t_t=t1=t2=D),t2!t1(cid:20)ft.AconstraintsystemCisamultiset1ofconstraintsoftheform(cid:15)(cid:11)0!(cid:11)00(cid:20)f(cid:11),(cid:15)(cid:12)(cid:20)b(cid:11),(cid:15)(cid:11)=(cid:11)0,and(cid:15)(cid:11)>(cid:11)0.where(cid:11),(cid:11)0,(cid:11)00,(cid:12)aretypevariablesoratypeconstantSorD.AsubstitutionV(ofgroundtypeexpressionsfortypevariables)isasolutionofCifthefollowingtwoconditionsareful(cid:12)lled:1.ForeveryconstraintofformVful(cid:12)ls(cid:11)0!(cid:11)00(cid:20)f(cid:11)V((cid:11)0!(cid:11)00)(cid:20)fV((cid:11))(cid:12)(cid:20)b(cid:11)V((cid:12))(cid:20)bV((cid:11))(cid:11)=(cid:11)0V((cid:11))=V((cid:11)0)(cid:11)>(cid:11)0V((cid:11))=D)V((cid:11)0)=D2.Foreverytypevariable(cid:11)notoccurringinCwehaveV((cid:11))=(cid:11).2WewriteSol(C)forthesetofallsolutionsofC.Wede(cid:12)netheconstraintsystemC(cid:28)(e)byinductionasfollows.1Usingmultisetsinsteadofsetsleadstoasimplerformulationandfasterimplementationoftheconstrainttransformationsrules2ThisconditionguaranteesthatsolutionsVandV0areequalwhenevertheirrestrictionstothevariablesoccurringinCareequal.
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Context: vte2andte2vte1.Discusstheimpactofdi(cid:11)erentchoicesone(cid:14)ciencyofthepartialevaluationprocessitself.2
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Context: 318ProgramAnalysisthiscase,ycangetboundtowhateverlambdastheargumentejcanevaluateto.Moreover,ycangetboundbyevaluatinganyoftheargumentexpressions.Inalambdaabstraction,ycangetboundinthebodye.Alambdaapplication(e1e2)bindsyifitisalambdavariablex‘ande1canevaluatetothelambdalabelled‘.Inthiscase,ycangetboundtowhateverlambdastheargumente2canevaluateto.Finally,aletexpressionbindsyifyisthesameasthevariablexbeingbound.15.2.3ClosureanalysisofaprogramClosureanalysisofaprogramshouldproduceasafedescription((cid:30),(cid:26))whichisaspreciseaspossible.Thedescriptionisobtainedastheleastsolutiontoasetofequationsspecifyingthesafetyrequirement.Asdescribedabove,(cid:30)shouldmapanamedfunctionfitothesetoflambdasthatitsbodycanevaluateto,thatis,Pe[[bodyi]](cid:30)(cid:26).Similarly,itshouldmapalambdalabel‘tothesetoflambdasthatthebodyeof(cid:21)x‘.ecanevaluateto,thatis,Pe[[e]](cid:30)(cid:26).Themap(cid:26)shouldmapafunctionvariablextothesetoflambdasthatitcanbeboundto,thatis,theunionofPv[[bodyi]](cid:30)(cid:26)xoverallfunctionbodiesbodyi.Similarly,(cid:26)shouldmapalambdavariablex‘tothesetoflambdasitcanbeboundto,thatis,theunionofPv[[bodyi]](cid:30)(cid:26)x‘overallfunctionbodiesbodyi;andlikewiseforthelet-variables.Theserequirementsaresummarizedbytheequationsbelow:(cid:30)fi=Pe[[bodyi]](cid:30)(cid:26)(cid:30)‘=Pe[[e]](cid:30)(cid:26)whereeisthebodyof(cid:21)x‘.e(cid:26)x=Sni=1Pv[[bodyi]](cid:30)(cid:26)xforeveryfunctionvariablex(cid:26)x‘=Sni=1Pv[[bodyi]](cid:30)(cid:26)x‘foreverylambdavariablex‘(cid:26)x=Sni=1Pv[[bodyi]](cid:30)(cid:26)xforeverylet-variablexAnysolutiontotheseequationsisasafeanalysisresult.Theleastsolutionisthemostprecise,safesolution.15.2.4ClosureanalysisasanabstractinterpretationTheclosureanalysisasdescribedabovecanbeunderstoodasanabstractinter-pretation.AnabstractvalueL2LabelSetisasetoflabels.Theabstractvaluesareorderedbyinclusion:L1issmallerthanL2ifandonlyifL1(cid:18)L2.ThismakesLabelSetalatticeandisveryreasonable,intuitively:asmallerlambdalabelsetismoreinformative,sinc
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Context: Exercises25911.10ExercisesExercise11.1Specializetheminiprintf()inExample11.1usingthemethodsdescribedinthischapter,i.e.transformtoCoreC,binding-timeanalysetoobtainatwo-levelCoreCprogram,andspecializeit.2Exercise11.2Inthetwo-levelCoreClanguage,bothastaticandadynamicgotoexists,eventhoughagotocanalwaysbekeptstatic.Findanexampleprograminwhichitisusefultoannotateagotodynamic2Exercise11.3IntheCoreClanguage,allloopsarerepresentedasL:if()...gotoL;.ConsideranextensionoftheCoreClanguageandthetwo-levelalgorithminFigure11.4toincludewhile.Discussadvantagesanddisadvantages.2Exercise11.4Considerthebinarysearchfunctionbinsearch().#defineN10intbinsearch(intn)fintlow,high;low=0;high=N-1;while(low<=high)fmid=(low+high)/2;if(table[mid]<n)high=mid-1;elseif(table[mid]>n)low=mid+1;elsereturnmid;ggAssumethatthearraytable[N]ofintegerisstatic.Specializewithrespecttoadynamicn.Predictthespeedup.IsthesizeoftheprogramrelatedtoN?2Exercise11.5TheSpeedupTheoreminChapter6statesthatmixcanatmostaccomplishlinearspeedup.DoesthetheoremholdforpartialevaluationofC?ExtendtospeedupanalysisinChapter6tocopewithfunctions.2Exercise11.6ConsidertheinferencerulefortheaddressoperatorinFigure11.3.Noticethatiftheexpressionevaluatestoadynamicvalue,thentheapplicationissuspended.Thisisdesirableinthecaseofanexpression&a[e]whereeisdynamic,butnotincaseof&xwherexisdynamic.Findwaystoremedythisproblem.2Exercise11.7Formalizethewell-annotatednessrequirementsforstatements,func-tions,andCoreCprograms.2Exercise11.8Developthecall-graphanalysisasdescribedinSection11.6.Developthepointeranalysis.2
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Context: Index(cid:11)-conversion,46(cid:11)e(Lambdamix),176V)(Lambdamix),179jsj(sizeof),127(cid:12)-reduction,46?(bottom),24,311,323[[]]L(meaningfunction),4[[(cid:1)]](syntax),147(cid:14)(Scheme1binding-timeanalysis),320(cid:14)-reduction,46(cid:17)-conversion,273(cid:20)b(Lambdamix),177(cid:20)f(Lambdamix),177v(Lambdamix),172(cid:14)(composition),25’=[[]](programminglanguage),336(cid:30)(Lambdamix),172(cid:30)(closureanalysis),316 (Scheme1binding-timeanalysis),320?(V)(variabledescription),303(cid:26)(closureanalysis),316(cid:28)(Scheme0binding-timeanalysis),108(cid:28)(Scheme1binding-timeanalysis),320>(top),310>(Lambdamix),177absentprojection,324abstractenvironment,310abstractfunction,311abstractinterpretation,106,309,310abstractsyntax,35abstractvalue,309abstractionimplementedbyfunction,166inlambdacalculus,45rule,374abstractionfunction,312acceptableprogrammingsystem,336Ackermann’sfunction,357additiverunningtimes,129admissibleprojection,324{326,328agreeingenvironment,185algorithm,25non-oblivious,286,290oblivious,285algorithmW,175AMIX,4analysisbinding-time,seebinding-timeanalysisbyabstractinterpretation,309call-graph,253closure,315dependency,198,302dependentonbindingtimes,152determinacy,198groundness,198livevariable,95pointer,254pointerbirth-place,244side-e(cid:11)ect,198size,302speedup,132Andersen,L.O.,131,153,229,368,371,372annotation-forgettingfunction,172annotations,105,151,194consistent,105,123anonymousftp,225,366instructionsfor,123Appel,A.,286applicativeorderreduction,48argumentexpression,102arityraising,224,332,371assemblerinterpreter,258assignment,369associativelaw,263atomicvalue,324406
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Context: econtextKofthereducedvalueisalsopassedtoreduce.ModifyingthehandlingofdynamicletdthenpropagatesthecontextKtothe(possiblystatic)bodyofthelet.Thisis
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Context: 18Introductionthatthereisatheoreticallimittotheamountofspeed-upthatcaningeneralbeexpectedfrompartialevaluation.Chapter7compareso(cid:15)inepartialevaluation(usingaseparatebinding-timeanalysisphase)toonlinepartialevaluation.Theo(cid:15)ineapproachappearsbene(cid:12)cialtoself-application,andallpartialevaluatorspresentedinthisbookareo(cid:15)ine.Finally,someadviceonconstructingaself-applicablepartialevaluatorforanewprogramminglanguageisgiven.PartIIIpresentspartialevaluatorsforfourlanguageswhicharestrongerinvar-iousrespectsthanthe(cid:13)owchartandrecursionequationlanguage.Chapter8describesaself-applicablepartialevaluatorfortheuntypedlambdacalculus,strongerthanthepreviouspartialevaluatorinthatitcandealwithhigher-orderfunctions.Itisseenthattypeinferenceprovidesasimpleandelegantwaytodobinding-timeanalysisforhigher-orderpartialevaluation.Chapter9describesaself-applicablepartialevaluatorforaPrologsubset,em-phasizingproblemsnotseenintheearlierchapters.Chapter10presentsapartialevaluator‘Similix’,whichhandlesasubstantialsubsetofSchemeincludingbothhigher-orderfunctionsandrestrictedsidee(cid:11)ects.Schemeisinterestingbecauseitisarealisticlanguage.Chapter11presentssometechniquesrequiredtodealwithlanguages(C,Pascal,etc.)wheretheprogrammingstylereliesonboth(recursive)functioncallsandaglobal,mutablestate,includingarraysandpointers.PartIVdiscussespracticalproblemsfrequentlyencounteredwhenusingpartialevaluation,andgivesseveralexamplesofitspracticalapplication.Chapter12showsthatsomesubjectprogramsarelessamenabletospeed-upbypartialevaluationthanothers,andpresentssometransformationthatimprovetheresultsofpartialevaluationwhilepreservingthemeaningofthesubjectprogram.Chapter13describesseveralapplicationsofpartialevaluationanddemonstratesthattheutilityofpartialevaluationisnotlimitedtocompilergeneration.PartVpresentsadvancedtopics,morebrie(cid:13)ythantheprevioussubjectsandwithreferencestocurrentliterature.Chapter14containsadiscussionofterminationinpartialevaluation.Analgo-rithmsu(cid:14)ci
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Context: ContentsviiIIIPartialEvaluationforStrongerLanguages1618PartialEvaluationfortheLambdaCalculus1638.1Thelambdacalculusandself-interpretation1648.2Partialevaluationusingatwo-levellambdacalculus1668.3Congruenceandconsistencyofannotations1698.4Binding-timeanalysis1728.5SimplicityversuspowerinLambdamix1738.6Binding-timeanalysisbytypeinference1758.7BTAbysolvingconstraints1758.8CorrectnessofLambdamix1838.9Exercises1909PartialEvaluationforProlog1929.1Anexample1959.2ThestructureofLogimix1969.3Conclusion2009.4Exercises20210AspectsofSimilix:APartialEvaluatorforaSubsetofScheme20410.1AnoverviewofSimilix20410.2Specializationwithrespecttofunctionalvalues21010.3Avoidingduplication21510.4Callunfoldingonthe(cid:13)y21710.5Continuation-basedreduction21810.6Handlingpartiallystaticstructures22310.7TheSimiliximplementation22510.8Exercises22511PartialEvaluationfortheCLanguage22911.1Introduction22911.2Specializationofcontrol(cid:13)ow23211.3Functionspecialization23411.4Datastructuresandtheirbinding-timeseparation23911.5PartialevaluationforCbytwo-levelexecution24511.6Separationofthebindingtimes25311.7Self-application,types,anddoubleencoding25611.8C-mix:apartialevaluatorforCprograms25611.9TowardspartialevaluationforfullAnsiC25811.10Exercises259
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Context: go-rithmsu(cid:14)cienttoguaranteethatprogramspecializationterminates(intheabsenceofin(cid:12)nitestaticloops)ispresentedandjusti(cid:12)ed,forthesimple(cid:13)owchartlanguageofChapter4.Chapter15explainstheprogramanalysismethodcalledabstractinterpretation,presentsaso-calledclosureanalysiswhichisusefulfordoinganalysisofhigher-orderprograms,andappliesthistoproduceahigher-orderbinding-timeanalysisforthepartialevaluatorinChapter10.Finally,itpresentsaprojection-basedapproachtobinding-timeanalysisofpartiallystaticdatastructures.Chapter16relatespartialevaluationtorecursivefunctiontheory,givesamoreabstractviewofprogramspecialization,anddiscussesthetypesofprogram-pro-
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Context: Binding-timeanalysisensuringtermination303(cid:13)owchart.A(cid:13)owdescriptionisofform(ppi,ppj):(d1,...,dm).Eachdkdescribesthedependencyofthevalueofvariablexkattheendofthepathonthevariablesx1...xmatthebeginningofthepath.VariabledescriptionsAvariabledescriptionhasoneofthreeforms:d=I(V),d=E(V),ord=D(V),whereV(cid:18)VarIndex=f1;...;mg.VDescisthesetofallvariabledescriptions.Whenavariablexiisdescribedbyd,thenitdependsonthosevariablesxjforwhichj2V.TheletterI,E,orDdescribestherelationbetweenthesizeofxiandthexj’s:DdenotesstrictlyDecreasing,Edenotesnon-increasing=lessthanorEqual,andIdenotes(possibly)Increasing.Also,whenavalueisbothincreasedanddecreasedweclassifyitasincreasing.Givena(cid:13)owdescriptionofform(ppi,ppj):d,thetablebelowsummarizeshowtointerprettheelementsofd(recallthatv#kselectsthek’thcomponentofv):dk=D(V)meansvj#k<vi#s,foralls2Vdk=E(V)meansvj#k(cid:20)vi#s,foralls2Vdk=I(V)meansvj#kdependsonvi#s,foralls2VWetake?(V)tomeaneitherI(V),E(V),orD(V).NotethespecialcaseV=fg.Thisimpliesthatxidependsonnovariablesandthusisconstant.Anydescription?(fg)wouldcorrectlydescribethee(cid:11)ectofanassignmentofconstantvaluesuchasxi:=42.FlowdescriptionsAssociatewithanytwoprogrampoints,ppiandppj,adescriptionofthevariablestogeta(cid:13)owdescriptionofform(ppi,ppj):d,whered=(d1,...,dm).Thetypeofa(cid:13)owdescriptionisthusPPoint(cid:2)PPoint(cid:2)Vdescm.A(cid:13)owdescriptionofthisformmeansthatinonepossiblecomputation(ppi,vi)!...!(ppj,vj)thedependencyofvjonviisdescribedby(d1,...,dm).Notethatwealsoincludecomputationsthatdonotstartattheinitialprogrampoint.14.3.3FlowdescriptionofallpossiblecomputationsWeshallnowde(cid:12)neasetof(cid:13)owdescriptionsFD:}(PPoint(cid:2)PPoint(cid:2)Vdescm)thatcapturesallpossiblecomputations.FDwillbeasetoftriples(ppi,ppj):d,whereddescribesthedependencyrelationsbetweenprogramvariablesattheendofonepossiblepathfromppitoppj,andthevariables’valuesatthestartofthatsamepath.NotethatFDis(cid:12)nitesincethereareonly(cid:12)nitelymanydi(cid:11)erenttriples(eventho
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Context: 142Chapter4  (cid:127)  Plugins, Preprocessors and Output Modulesuse of /etc/syslog.conf file on UNIX systems. A typical syslog.conf file onRedHat Linux 7.3 system follows. As you can see from this file, a log file is defined foreach type of facility. Most of the messages go into /var/log/messages files.# Log all kernel messages to the console.# Logging much else clutters up the screen.kern.*             /dev/console# Log anything (except mail) of level info or higher.# Don't log private authentication messages!*.info;mail.none;news.none;authpriv.none;cron.none              /var/log/messages# The authpriv file has restricted access.authpriv.*          /var/log/secure# Log all the mail messages in one place.mail.*             /var/log/maillog# Log cron stuffcron.*             /var/log/cron# Everybody gets emergency messages*.emerg            *# Save news errors of level crit and higher in a special file.uucp,news.crit     /var/log/spooler# Save boot messages also to boot.loglocal7.*           /var/log/boot.log## INN#news.=crit         /var/log/news/news.critnews.=err          /var/log/news/news.errnews.notice        /var/log/news/news.noticeIf you want to send different types of alerts using different facilities or priorities,you can define your own actions using the ruletype keyword as mentioned earlier. Afterdefining these rule types, you can use them in your rules as actions. As you will remem-ber from previous discussions, the first word in each rule is the action part.
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Context: 408Indexcomposition,25,338symbolic,338,360computationduplication,119computationalstate,77partial,77computergraphics,278concretizationfunction,312con(cid:12)guration,369con(cid:13)uenceproperty,349congruence,109forpointers,244uniform,77congruent,103conspointanalysis,224Consel,C.,130,150,218,224,264,270,368,370{373consistentannotation,105,123constantpropagation,286constraint,105dependencyrules,181equational,177,181inequality,177,181normalform,176normalization,179,180reduction,180usedforbinding-timeanalysis,175constraintsystem,177construction,31constructor,31,325context,348continuation,219continuationpassingstyle,116,218,270conversionto,270advantages,270disadvantages,272continuationvariable,238continuation-basedreduction,218contraction,369controlfunction,57controloperators(Prolog),193CoreC,245two-level,246,247correctnessfold/unfold,354Courcelle,B.,354Cousot,P.,310Cousot,R.,310CPS,seecontinuationpassingstylecurried,28Curry,H.B.,28Danvy,O.,130,150,204,218,264,270,272,368,370{373Darlington,J.,347,350,374datadivision,98datatype,32,325deSchreye,D.,371deadstaticvariable,95debuggerbinding-time,151decreasedinloop,302de(cid:12)nition,3,351,374de(cid:12)nitionofpartialevaluation,4deforestation,358,360dependencyanalysis,198,302dependencyconstraintrules,181dependentsum,332dependsalongpath,302overassignmentsequence,302depth-(cid:12)rstspecialization,236determinacyanalysis,198DFAgenerationof,196,268discardingofside-e(cid:11)ects,119divideandconquer,281division,77,102(cid:12)nite,83monovariant,96,103pointwise,94polyvariant,96,103uniform,77uniformlycongruent,77doubleappend,351doubleencoding,256doubleself-application,14driving,369dubiousvariable,301duplicable,119duplication,119harmless,121ofside-e(cid:11)ects,119D(V)(strictlydecreasing),303Dybkj(cid:26)r,H.,141,372dynamic,77,102,166,197always,149context,111,112,208control(cid:13)ow,233de(cid:12)nitely,145goal,197memoryallocation,243statement,230variable,301Earley,J.,8,372,374
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Context: 302TerminationofPartialEvaluation14.3.1ImportantconceptsSyntaxandsemanticsRecallthata(cid:13)owchartprogramwithinputvariablesx1...xkconsistsofareadstatementreadx1...xk;followedbyasequenceoflabelledbasicblocks:l0:bb0l1:bb1...ln:bbn.Iftheprogramhasvariablesx1,...,xm(m(cid:21)k)thenastorevisrepresentedbyanm-tupleofvaluesv=(v1,...,vm).Belowweshallusethemathematicalsemanticsfor(cid:13)owchartprogramsshowninSection3.3.3.DependencyVariablexdependsonvariableyovertheone-assignmentsequencex:=eifyoccursine.Further,xdependsonyoversequenceseq1;seq2ifoneofthefollowingconditionsholds:1.zdependsonyoverseq1andxdependsonzoverseq2;2.xdependsonyoverseq1andxisnotassignedinseq2.Variablexdependsonvariableyalongapathoflabelsl1,l2,...,liinthe(cid:13)owcharti(cid:11)xdependsonyovertheconcatenationoftheassignmentsatl1,....LoopsAloopisasequenceoflabelsl1,l2,...,liwherel1=li.Notethatwedonotdisallowlj=l1for1(cid:20)j(cid:20)i.Avariableyisincreasedinaloopwhenydependsonyalongtheloopanditsvaluegrowsforeachiteration.Similarly,xisdecreasedinaloopifxdependsonxalongtheloopanditsvaluediminishesforeachiteration.InExample14.1,yisincreasedandxisdecreasedintheloop.Example14.2Considerthefollowingprogramfragment:a:x:=x-1;y:=y+2;ife1gotoaelseb;b:x:=x+2;y:=y-1;ife2gotoaelseb;xisdecreasedandyisincreasedintheloopa,a.Notethatbothvariablesareincreasedintheloopa,b,a.Bothvariablesshouldbeclassi(cid:12)edasdynamicnomatterwhattherestofprogramlookslike.214.3.2SizeanddependencyanalysisInthissectionweanalysethedependenciesandsizevariancesamongtheprogramvariablesx1...xmalongvariousprogramcontrolpathsppi,ppi+1,...,ppjinthe
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Context: 320ProgramAnalysis15.3.1Binding-timeanalysismapsAsintheScheme0binding-timeanalysis,thebinding-timedomainisBindingTime=fS;DgwithS<D.Theanalysisusesthreemaps ,(cid:28),and(cid:14),whoseintendedmeaningsare fi=thebindingtimeofthebodyoffi x‘=thebindingtimeofthebodyeof(cid:21)x‘.e(cid:28)xij=thebindingtimeoffunctionvariablexij(cid:28)x‘=thebindingtimeoflambdax‘(cid:28)x=thebindingtimeoflet-variablex(cid:14)‘=Dif(cid:21)x‘.eneedstobelifted,SotherwiseThus mapsalambdatothebindingtimeoftheresultofapplyingit,and(cid:28)isabinding-timeenvironmentthatmapsavariable(whetherboundbyanamedfunction,alambda,oralet),toitsbindingtime.Theuseofthesemapsissimilartothatof(cid:30)and(cid:26)intheclosureanalysisabove.Themap(cid:14)isusedtorecordthelambdasthatmayappearindynamiccontexts.15.3.2Binding-timeanalysisfunctionsTheanalysisconsistsofabinding-timeanalysisfunctionBeandabinding-timepropagationfunctionBv,akintothefunctionsBeandBvintheScheme0binding-timeanalysis.Inaddition,thereisacontextpropagationfunctionBdfordetectionoflambdasindynamiccontexts.AnapplicationBe[[e]] (cid:28)(cid:14)oftheBefunction(cid:12)ndsthebindingtimeofexpres-sioneinbinding-timeenvironment(cid:28).TheBefunctionisde(cid:12)nedinFigure15.3.Equationsone,two,threeand(cid:12)veareexplainedasforScheme0.Equationfour(functioncall)statesthattheresultofafunctioncallisdynamicifthecalledfunc-tion’sbodyis.Thebindingtimecannolongerbedeterminedfromthearguments,sinceastaticargumentmaybeapartiallystaticlambdacontainingfreedynamicvariables.Equationsixstatesthatalambdaexpressionisdynamicifitmayappearinadynamiccontext,andstaticotherwise.Equationsevensaysthatthebindingtimeofahigher-orderapplication(e1e2)isdynamicife1is,orifanylambdathate1canevaluatetogivesadynamicresultwhenapplied.Equationeightsaysthatthebindingtimeofaletexpressionisdynamiciftheboundexpressione1orthebodyeis.AnapplicationBv[[e]] (cid:28)(cid:14)y(cid:12)ndsthebindingtimeofthevaluesassignedtovari-ableyduringevaluationofexpressione,whereymaybeafunctionvariablexij,alambdavariablex‘,oraletvariablex.TheBvfunctionisde(cid:12)nedinFigure15.4andisverysimilarinstructureandpurposetofunctionPvintheclosureanalysis.AnapplicationBdd[[e]] (cid:28)(cid:14)‘treturnsthecontext(SorD)oflambda(cid:21)x‘....
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Context: Chapter12FileSystemsWearenotrequiredtohavefilesystemsinouroperatingsystem,butitisaveryusableabstraction,anditoftenplaysacentralpartofmanyoperatingsystems,especiallyUNIX-likeoperatingsystems.Beforewestarttheprocessofsupportingmultipleprocessesandsystemcallswemightwanttoconsiderimplementingasimplefilesystem.WhyaFileSystem?HowdowespecifywhatprogramstoruninourOS?Whichisthefirstprogramtorun?Howdoprogramsoutputdataorreadinput?InUNIX-likesystems,withtheiralmost-everything-is-a-fileconvention,theseproblemsaresolvedbythefilesystem.(ItmightalsobeinterestingtoreadabitaboutthePlan9project,whichtakesthisideaonestepfurther.)ASimpleRead-OnlyFileSystemThesimplestfilesystemmightbewhatwealreadyhave-onefile,existingonlyinRAM,loadedbyGRUBbeforethekernelstarts.Whenthekernelandoperatingsystemgrowsthisisprobablytoolimiting.Afilesystemthatisslightlymoreadvancedthanjustthebitsofonefileisafilewithmetadata.Themetadatacandescribethetypeofthefile,thesizeofthefileandsoon.Autilityprogramcanbecreatedthatrunsatbuildtime,addingthismetadatatoafile.Thisway,a“filesysteminafile”canbeconstructedbyconcatenatingseveralfileswithmetadataintoonelargefile.Theresultofthistechniqueisaread-onlyfilesystemthatresidesinmemory(onceGRUBhasloadedthefile).Theprogramconstructingthefilesystemcantraverseadirectoryonthehostsystemandaddallsubdirectoriesandfilesaspartofthetargetfilesystem.Eachobjectinthefilesystem(directoryorfile)canconsistofaheaderandabody,wherethebodyofafileistheactualfileandthebodyofadirectoryisalistofentries-namesand“addresses”ofotherfilesanddirectories.Eachobjectinthisfilesystemwillbecomecontiguous,sotheywillbeeasytoreadfrommemoryforthekernel.Allobjectswillalsohaveafixedsize(exceptforthelastone,whichcangrow),thereforeitisdifficulttoaddnewfilesormodifyexistingones.69
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Context: Criticalassessment151.5.5HierarchiesofmetalanguagesAmodernapproachtosolvingawide-spectrumproblemistodeviseauser-orientedlanguagetoexpresscomputationalrequests,viz.thewidespreadinterestinexpertsystems.Aprocessorforsuchalanguageusuallyworksinterpretively,alternatingbetweenreadinganddecipheringtheuser’srequests,consultingdatabases,anddoingproblem-relatedcomputing|anobviousopportunitytooptimizebypartialevaluation.Suchsystemsareoftenconstructedusingahierarchyofmetalanguages,eachcontrollingthesequenceandchoiceofoperationsatthenextlowerlevel[234].Here,e(cid:14)ciencyproblemsareyetmoreserioussinceeachinterpretationlayermultipliescomputationtimebyasigni(cid:12)cantfactor.Weshallseethatpartialevaluationallowsonetousemetaprogrammingwithoutorder-of-magnitudelossofe(cid:14)ciency.1.6CriticalassessmentPartialevaluationandself-applicationhavemanypromisingapplications,andworkwellinpracticeforgeneratingprogramgenerators,e.g.compilersandcompilergen-erators,andotherprogramtransformers,forexamplestylechangersandinstru-menters.Theyare,however,stillfarfromperfectlyunderstoodineithertheoryorpractice.Signi(cid:12)cantproblemsremain,andweconcludebylistingsomeofthem.GreaterautomationanduserconvenienceTheusershouldnotneedtogiveadviceonunfoldingorongeneralization,thatistosay,wherestaticallycomputablevaluesshouldberegardedasdynamic.(Suchadviceisrequiredinsomecurrentsystemstoavoidconstructinglargeorin(cid:12)niteoutputprograms.)Theusershouldnotbeforcedtounderstandthelogicofaprogramresultingfromspecialization.Ananalogyisthatonealmostneverlooksatacompiler-generatedtargetprogram,oraYacc-generatedparser.Further,usersshouldn’tneedtounderstandhowthepartialevaluatorworks.Ifpartialevaluationistobeusedbynon-specialistsinthe(cid:12)eld,itisessentialthattheuserthinksasmuchaspossibleabouttheproblemheorsheistryingtosolve,andaslittleaspossibleaboutthetoolbeingusedtoaiditssolution.Aconsequenceisthatdebuggingfacilitiesandinterfacesthatgivefeedbackaboutthesubjectprogram’sbinding-timeseparationareessentialforuseb
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Context: 226AspectsofSimilix:APartialEvaluatorforaSubsetofSchemeexpressionsanddobinding-timeanalysisundertheassumptionthatxisdynamic.Annotatetheprogramaccordingly.Analysethenumberofoccurrencesofdynamiclet-boundvariables,andannotatetheletexpressionsaccordingly.(define(fx)(if(zero?x)(+(h(lambda(y)(+y1)))42)(+(h(lambda(y)(+y1)))(h(lambda(y)(+yx))))))(define(hg)(+(g17)(g42)))2Exercise10.2Executethepreprocessphaseforthefollowingprogram,andspe-cializef1undertheassumptionthatxisdynamic.Repeattheprocesswithxstaticandspecializewithrespecttox=25.(define(f1x)(f2(lambda(y)(if(<xy)xy))))(define(f2g)(+(g17)(g42)))2Exercise10.3Specializefinthefollowingprogramwithxdynamicwithoutin-sertingidentitylet-expression.Thenspecializeagainnowwiththeidentitylet-expressionsinserted.Whatisgainedbyinsertingidentitylet-expression?(define(fx)(g(fakx)(fak(+x5))(power28)))(define(gxyz)(+xxyyzz))(define(fakx)(if(equal?x1)1(*x(fak(-x1)))))(define(powerxn)(if(equal?n0)x(*x(powerx(-n1)))))2Exercise10.4ConsidertheappendfunctionbelowandassumethattruthvaluesandconditionalsareencodedasdescribedinSection10.4,thatis,(null?xs)re-turnseither(lambda(x)(lambda(y)x))or(lambda(x)(lambda(y)y)).Tryspecializing(whiledoingcallunfoldingonthe(cid:13)y)withrespecttoxsdynamicandysstaticwithoutinsertingspecializationpointsfordynamiclambdas.Whydoesthestrategyforcallunfoldingonthe(cid:13)yfail?(define(appendxsys)((((null?xs)(lambda(z)ys))(lambda(z)(cons(carxs)(append(cdrxs)ys))))’()))2
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Context: Rule Options
91
# $Id: classification.config,v 1.10 2002/08/11 23:37:18 cazz Exp $
# The following includes information for prioritizing rules
# 
# Each classification includes a shortname, a description, and a 
default
# priority for that classification.
Figure 3-3 Use of the classification keyword in displaying Snort alerts inside ACID window.
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Context: ContentsPrefacex1Introduction11.1Partialevaluation=programspecialization11.2Whydopartialevaluation?51.3Computationinonestageormore71.4Partialevaluationandcompilation111.5Automaticprogramgeneration131.6Criticalassessment151.7Overviewofthebook17IFundamentalConceptsinProgrammingLanguages212Functions,Types,andExpressions232.1Functions232.2Typesinprogramminglanguages262.3Recursivedatatypes322.4Summary362.5Exercises373ProgrammingLanguagesandInterpreters383.1Interpreters,compilers,andrunningtimes383.2Theuntypedlambdacalculus:syntaxandsemantics433.3Threemini-languages503.4Compilingcompilers583.5Thecentralproblemsofcompilation603.6Summary61v
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Context: Partialevaluationbyfold/unfold355setofrulesthathavenotyetbeenprocessed.InitiallyPendinghasonlyonegoalrule,representingacalltotheprogramonunspeci(cid:12)edinput.EachruleinPendingisofformL->RwheretermRhasasaninstancesometermobtainedwhilesymbolicallyexecutingtheoriginalprogram.(ThisisessentiallythesameastoTurchin’sbasiccon(cid:12)gurations[263].)Lde(cid:12)nesanewfunction,toappearinthetransformedprogram.RulesinPendingareprocessedoneatatime,sometimescausingnewrulestobeaddedtoOutorPending.Thealgorithmstopswhennounprocessedrulesremain.Aninformalalgorithminvariant:therulesetOut[PendingisconsistentwiththemeaningoftheoriginalprogramPgm.When(cid:12)nished,Outisthetransformedprogram.WeimplicitlyassumevariablenamestandardizationsoPendingandOutwillnevercontainrulesL->RandL0->R0di(cid:11)eringonlyinthenamesofleftsidevariables.Thegeneralmethodisasfollows:1.Simulatetheprogram’scomputation,usingvariablesforunknownvalues.Allowforallpossibleprograminputs.2.Ateachstagelookforapotentialredex.Ifvariablesareinsu(cid:14)cienttocontinuethesimulation,instantiatetheminallpossiblyrelevantways.3.Unfoldaredexwheneverpossible.Rationale:computationdoneattransfor-mationtimeneednotbedoneinthetransformedprogram.4.Reasonsnottounfold:(cid:15)Thechosensemanticswouldnotunfold.(cid:15)Thecurrentcallcon(cid:12)gurationhasbeenseenbefore.Unfoldingtooliberallycouldcauseloopingattransformationtime.Anappropriateresponseistospecializeormemoize,i.e.togeneratecodetocallthecon(cid:12)gurationseenbefore(afoldstep)2.(cid:15)Thereisinsu(cid:14)cientinformationtodecidewhichrulewouldbeappliedtotheredex.Response:makeanewfunctionde(cid:12)nitionfP1...Pn->B;foldthepotentialredexintoacalltof;andinstantiatethenewruleasin2toaccountforallpossiblerulematches.17.3Partialevaluationbyfold/unfoldFollowingisasketchofasimpleonlinepartialevaluatorforthecurrentlanguagewithacontrolstrategyusedearlier:unfoldaslongasnodynamictestsoccur,andspecializecallstocon(cid:12)gurationsseenbefore.Weassumeacall-by-valuesemantics,asinthepartialevaluat
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Context: 414Indexsubjectprogram,2,71substitution,46,348suitsavariable,171SU(l)(speedup),134sumofprojections,326type,31Sundaresh,R.S.,370,373supercompilation,358,369superlinearspeedup,347falsely,130impossible,131suspension,54symboliccomposition,360computation,3runtimeaddress,242syntaxtree,35tp(s;d)(runningtime),41,127tX(type),339table-directedinput,284tag,31,325taggedvalue,31tagginginonlinepartialevaluation,147Takeuchi,A.,369tamingofself-application,153targetprogram,8,76terminationensuredbybinding-timeanalysis,304ofo(cid:15)inepartialevaluation,298ofonlinepartialevaluation,297ofpartialevaluation,297thetrick,93,116,266,271theoremsforfree,274thirdFutamuraprojection,14,91thunk,54Tofte,M.,375top(>),310top-levelredex,47totalfunction,23,26transitioncompression,81,99,104onthe(cid:13)y,82,93separatephase,82trickthe,93,116,266,271truthvaluesB,26tuplingfunction,336Turchin,V.F.,358,367,369Turinginterpreter,73Turingmachine,73,366enumeration,335oblivious,285program,335two-levelCoreC,246executionofCoreC,250lambdacalculus,166Scheme0,110Scheme1,207syntax,105type,171typesystem(Lambdamix),171type,26assumption,29environment,29,171function,26inference,29,123,175rule,30,339ofcompiler,337ofinterpreter,337ofpartialevaluator,337product,26semanticsof,26,34,339sum,31systemcombined,176two-level,171two-level,171unfoldreasonsnotto,355unfolding,3,104,237,351,374strategy,118uni(cid:12)cation,backwards,197uniformcongruence,77,331uniformdivision,77uniformprojection,327universalfunctionproperty,336updatingoffunction,25user-orientedlanguage,139,283vanHarmelen,F.,373variablecapture,47free,46splitting,332,371variabledescription,303variableonlyrequirement,362variantrecord,31vectorspaces,340Venken,R.,369videogames,277
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Context: Index409eliminablecommand,91Emanuelson,P.,372,373end-con(cid:12)guration,238environment,42,51,164agreeing,185implementedbyfunction,166type,171equalityoffunctions,25equationalconstraintrules,181equivalentoperationally,340semantically,340Ershov,A.P.,10,286,367,368,370,372eurekastep,351E(V)(lessorequal),303experimentalmodelling,280explicators,237expression,164extensionallyequal,209falselysuperlinearspeedup,130FD(setof(cid:13)owdescriptions),303Fibonaccitransformationof,353(cid:12)bre,332Filinski,A.,218,270(cid:12)nitedivision,83(cid:12)nitelist,32(cid:12)nitelydownwardsclosed,300(cid:12)niteness,125bygeneralization,362bysyntacticrestrictions,362(cid:12)rstFutamuraprojection,13,75,344(cid:12)rst-order,26(cid:12)rstorder(type),338Fischer,M.J.,218(cid:12)xed-pointoperatorexplicit,164(cid:13)atten,362(cid:13)owchartprogram,55,70interpreterfor,56mathematicalsemanticsfor,57partialevaluationof,67(cid:13)owdescription,303fold/unfoldcorrectnessof,354deterministic,354partialevaluationby,355transformations,350folding,3,351,374formalparameter,44Frauendorf,F.,194,195,370freeoccurrence,46variable,46ftp,225,366instructionsfor,123Fujita,H.,194,369Fuller,D.A.,194,195,371fullyabstract,339fullystaticfunction,212functionbody,44,102equality,25fullystatic,212partial,24partiallystatic,212specialization,234,338total,23type,26functionvariable,315Furukawa,K.,194Futamuraprojection,75,256,367(cid:12)rst,13,75,344second,13,86,344third,14,91Futamura,Y.,367Gallagher,J.,373Gaudel,M.C.,375generalization,83,363,369poorman’s,152,298generatingextension,10,125generatingoptimizingcompilers,150Gengler,M.,371Gl(cid:127)uck,R.,145,369,372,373Goad,C.,374goaldynamic,197specializationof,198static,197goalfunction,102Gomard,C.K.,131,163,270,368,369,371,372groundterm,348groundnessanalysis,198Gurevich,Y.,131Gustavson,F.G.,280,374Guzowski,M.,368H(Lambdamix),186Hannan,J.,12,373Hansen,T.A.,132,372Haraldsson,A.,367,368,373harmlessduplication,121Henglein,F.,151,176,371
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Context: ddCHECKSUM;andthechecksumloader:;theloaderlabel(definedasentrypointinlinkerscript)moveax,0xCAFEBABE;placethenumber0xCAFEBABEintheregistereax.loop:jmp.loop;loopforeverTheonlythingthisOSwilldoiswritetheveryspecificnumber0xCAFEBABEtotheeaxregister.Itisveryunlikelythatthenumber0xCAFEBABEwouldbeintheeaxregisteriftheOSdidnotputitthere.Thefileloader.scanbecompiledintoa32bitsELF[18]objectfilewiththefollowingcommand:nasm-felf32loader.sLinkingtheKernelThecodemustnowbelinkedtoproduceanexecutablefile,whichrequiressomeextrathoughtcomparedtowhenlinkingmostprograms.WewantGRUBtoloadthekernelatamemoryaddresslargerthanorequalto0x00100000(1megabyte(MB)),becauseaddresseslowerthan1MBareusedbyGRUBitself,BIOSandmemory-mappedI/O.Therefore,thefollowinglinkerscriptisneeded(writtenforGNULD[20]):ENTRY(loader)/*thenameoftheentrylabel*/SECTIONS{.=0x00100000;/*thecodeshouldbeloadedat1MB*/.textALIGN(0x1000):/*alignat4KB*/{*(.text)/*alltextsectionsfromallfiles*/}.rodataALIGN(0x1000):/*alignat4KB*/{*(.rodata*)/*allread-onlydatasectionsfromallfiles*/}.dataALIGN(0x1000):/*alignat4KB*/{*(.data)/*alldatasectionsfromallfiles*/}.bssALIGN(0x1000):/*alignat4KB*/{*(COMMON)/*allCOMMONsectionsfromallfiles*/*(.bss)/*allbsssectionsfromallfiles*/}}Savethelinkerscriptintoafilecalledlink.ld.Theexecutablecannowbelinkedwiththefollowingcommand:14
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Context: 124Chapter3  (cid:127)  Working with Snort Rules       Old: alert udp $EXTERNAL_NET any -> $HOME_NET 64 (msg:"TFTP Put"; content:"|00 02|"; offset:0; depth:2; reference:cve,CVE-1999-0183; reference:arachnids,148; classtype:bad-unknown; sid:518; rev:3;)       New: alert udp $EXTERNAL_NET any -> $HOME_NET 69 (msg:"TFTP Put"; content:"|00 02|"; offset:0; depth:2; reference:cve,CVE-1999-0183; reference:arachnids,148; classtype:bad-unknown; sid:518; rev:3;)[*] Non-rule lines added/removed: [*]    None.[*] Added files: [*]    None.The tool gives you a detailed report of actions taken during the update process.You can test this by deleting and modifying some rules and running the tool again. Thefollowing is a partial output seen when Oinkmaster adds and updates some rules.Comparing new files to the old ones... done.[***] Results from Oinkmaster started Wed Jul 10 12:25:37 2002 [***][*] Rules added/removed/modified: [*]  [+++]           Added:           [+++]     -> File "tftp.rules":       alert udp any any -> any 69 (msg:"TFTP GET shadow"; content: "|0001|"; offset:0; depth:2; content:"shadow"; nocase; classtype:successful-admin; sid:1442; rev:1;)       alert udp any any -> any 69 (msg:"TFTP GET passwd"; content: "|0001|"; offset:0; depth:2; content:"passwd"; nocase; classtype:successful-admin; sid:1443; rev:1;)       alert udp $EXTERNAL_NET any -> $HOME_NET 69 (msg:"TFTP parent directory"; content:".."; reference:arachnids,137; reference:cve,CVE-1999-0183; classtype:bad-unknown; sid:519; rev:1;)  [///]       Modified active:     [///]     -> File "tftp.rules":       Old: alert udp $EXTERNAL_NET any -> $HOME_NET 64 (msg:"TFTP Put"; content:"|00 02|"; offset:0; depth:2; reference:cve,CVE-1999-0183; reference:arachnids,148; classtype:bad-unknown; sid:518; rev:3;)       New: alert udp $EXTERNAL_NET any -> $HOME_NET 69 (msg:"TFTP Put"; content:"|00 02|"; offset:0; depth:2; reference:cve,CVE-1999-0183; reference:arachnids,148; classtype:bad-unknown; sid:518; rev:3;)[*] Non-rule lines added/removed: [*]    None.[*] Added files: [*]    None.
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Context: Supercompilationanddeforestation361MainprogramOut:=fg;Pending:=fLinitial->Tinitialg;while9anunmarkedruleg0vx1...xm->e[gvt1...tn]2PendingdoMarkit;forallgPv1...vn->R2ProgramdoAddg0Px1...xm->T[[(e[gPt1...tn])]]toOut;De(cid:12)nitionofTT[[x]]=xT[[ct1...tn]]=cT[[t1]]...T[[tn]]T[[e[ht1...tn]]]=letruleL->R2Pgmwith(cid:18)L=ht1...tninT[[e[(cid:18)R]]]T[[e[g(ct01...t0m)t1...tn]]]=letruleL->R2Pgmwith(cid:18)L=e[g(ct01...t0m)t1...tn]inT[[e[(cid:18)R]]]T[[e[gvt1...tn]]]=letL0->R0=makenew(e[gvt1...tn])inifL0->R02PendingthenL0elseAddL0->R0toPending;resultisL0De(cid:12)nitionofMakenewMakenew(e[gvt1...tn])=g0vx1...xm->e[gvt1...tn]whereg0isanewfunctionname,dependingonlyone[gvt1...tn],andfx1,...,xmg=FreeVariables(e[gvt1...tn])nfvgFigure17.5:Deforestationbyfold/unfold.covercomputationsonallpossibleinputs.By1,oneinstantiatesvariablevinalazycontexte[gv...]forallpossiblymatchinggrules.Componentsofaconstructionct1...tnsoobtainedmaybeprocessedfurthersincethevaluesoft1...tncanpossiblybedemanded.Assumption2impliesthatunfoldingandinstantiationmayonlybedoneifforcedbylazyevaluation.By1,acallnotinsideanothercallmaybeunfolded,evenifinsideaconstructorterm.Foranoutermostgcall(g(h...)...),thevalueofhwillbeneededinordertodeterminewhichgrulecanbeapplied.Thusthehcallmaybeunfoldedattransformationtime.Summingup,whenoutermostcallunfoldingisimpossiblethealgorithminstan-tiatesorunfoldscallsina(cid:12)rstgargumentasneededtobeabletocontinue.Theresultisaprogramwithcallcon(cid:12)gurationse[gvt1...tn],perhapsnested.New
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Context: concreteexampletoillustratetheproblemisthe‘(cid:13)atten’program(ais‘append’frombefore):
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Context: 380TheSelf-ApplicableScheme0SpecializerA.3ThecomponentsoftheScheme0specializerThe(cid:12)lescheme0.ssde(cid:12)nessyntacticshorthandsandthemainfunctionsusedforexperiments.;File"scheme0.ss"--Globaldefinitionsandsyntacticshorthands;Partialevaluatorforfirstorderfunctionallanguage;SyntacticsugarforScheme0.Format:listoftriples(abbrevvarterm),;meaning:expand(abbreve)intotermwithesubstitutedforvar.(definesugars’(;Selectfrome==(tage1e2e3...)(tage(hde))(e1e(hd(tle)))(e2e(hd(tl(tle))))(e3e(hd(tl(tl(tle)))));Selectfrome==(callfunnamesfunargsdfunargs)(funnamee(hd(tle)))(sfunargse(hd(tl(tle))))(dfunargse(hd(tl(tl(tle)))));Selectfrome==(call/opfunname.callargs)(callargse(tl(tle)));Selectfromdef==(define(namesvardvar)body)(namedef(hd(hd(tldef))))(svardef(hd(tl(hd(tldef)))))(dvardef(hd(tl(tl(hd(tldef))))))(bodydef(hd(tl(tldef))));Selectfromdef==(define(name.var)body)(vardef(tl(hd(tldef))));IntroducingScheme0baseoperationshorthands(hde(opcare))(tle(opcdre))));Desugar:FromsugaredScheme0toplainScheme0,usingthe"sugars"table(define(desugarprogram)(define(desugesub)(if(or(null?e)(number?e))e(if(atom?e)(let((var-term(assocesub)))(ifvar-term(cdrvar-term)e))(if(equal?(care)’quote)e(if(equal?(care)’if)(cons’if(desug*(cdre)sub))(if(member(care)’(callop))(cons(care)(cons(funnamee)(desug*(callargse)sub)))(if(equal?(care)’::)(list’op’cons(desug(e1e)sub)(desug(e2e)sub))(if(equal?(care)’list);(liste1e2...en)(foldr(lambda(elst)(list’op’cons(desugesub)lst))()(cdre))(if(equal?(care)’slet);(slet(varexp)body)(desug(caddre)(cons(cons(caadre)(desug(cadadre)sub))sub));elseitmustbeanabbreviation(let((expansion(assoc(care)sugars)))(ifexpansion(let((var(cadrexpansion))(term(caddrexpansion)))(desug(desugterm(cons(consvar(cadre))sub))sub))(error’desugar"Unknownoperatorormacro:~s"(care))
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Context: rrespondingcompletionsarerespectively
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Context: Exercises1914.SpecializesintwithrespecttothepowerprograminSection8.1.Thefreevariablese’and(cid:26)’ofsintshallhavethefollowingstaticvalues:e’=((fix(cid:21)p.(cid:21)n’.(cid:21)x’....)nx)and(cid:26)’=[n7!n,x7!x].2Exercise8.5ImplementthepartialevaluatorfromFigure8.3inaprogramminglanguageofyourownchoice.2Exercise8.6*ImplementthepartialevaluatorfromFigure8.3inthelambdacalculus.Itmightbeagoodideatoimplementtheself-interpreter(cid:12)rstandthenextendittohandlethetwo-levelexpressions.Usethepartialevaluatortospecializesintwithrespecttovariouslambdaexpressions.Isthepartialevaluatoroptimal?Tryself-applicationofthepartialevaluator.2Exercise8.7Giventheexpression((cid:21)x.x)ywith(cid:28)(y)=S,generateconstraints,normalizetheconstraintset,andthengiveaminimalcompletionfortheexpression.Repeattheprocessforthesameexpressionbutwith(cid:28)(y)=D.2Exercise8.8*ImplementthebindingtimeanalysisforthelambdacalculusbyconstraintsolvingasdescribedinSection8.7.The‘sledgehammer’approachfornormalizingtheconstraintsetisperfectlysuitedforaprototypeimplementation.Justnotethatitcanbedoneinalmost-lineartime,O(n(cid:11)(n;n)),where(cid:11)isaninverseofAckermann’sfunction[114].2Exercise8.91.AttheendofSection8.1islistedhowthelambdacalculusinterpreterinSection3.1hasbeenrevisedtoobtainthatinFigure8.1.Howdotheserevisionsa(cid:11)ecttheresidualprogramsproducedbypartialevaluationoftheseinterpreters?2.Whatfurtherrevisionswouldbenecessarytoachieveoptimality?2Exercise8.10Assumethataself-interpreterop-sintisavailablewiththepropertythatLambdamixisruledoptimalwhenthisparticularself-interpreterisusedintheDe(cid:12)nition6.4.DoesitfollowfromthisoptimalitypropertyandTheorem8.1thatop-sintisacorrectself-interpreterwhenEisusedasthecanonicalsemanticsforlambdaexpressions?2Exercise8.11ElaboratetheproofforTheorem8.1byprovingthecasesforstaticanddynamicapplications,staticanddynamicconditionals,anddynamic(cid:12)xed-pointoperators.2Exercise8.12Provethatresidualprogramsareidenticalforcompletionste1andte2ifte1vte2andte2vte1.Discu
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Context: viContents3.7Exercises62IIPrinciplesofPartialEvaluation654PartialEvaluationforaFlowChartLanguage674.1Introduction684.2Whatispartialevaluation?694.3Partialevaluationandcompilation734.4Programspecializationtechniques764.5Algorithmsusedinmix854.6ThesecondFutamuraprojection:compilergeneration864.7Generatingacompilergenerator:mix3914.8Thetricksunderthecarpet914.9Thegranularityofbinding-timeanalysis944.10Overviewofmixperformance974.11Summaryandamoreabstractperspective984.12Exercises995PartialEvaluationforaFirst-OrderFunctionalLanguage1015.1From(cid:13)owchartstofunctions1015.2Binding-timeanalysisbyabstractinterpretation1065.3Addingannotations1105.4SpecializationalgorithmforScheme01135.5Callunfoldingonthe(cid:13)y1185.6Implementation1225.7Usingtyperulesforbinding-timechecking1235.8Constructingthegeneratingextension1255.9Exercises1256E(cid:14)ciency,Speedup,andOptimality1276.1De(cid:12)ningandmeasuringspeedup1276.2Flowchartmixgiveslinearspeedup1306.3Speedupanalysis1326.4Optimalityofmix1386.5Hierarchiesofmeta-languages1396.6Exercises1417Online,O(cid:15)ine,andSelf-application1447.1Decisionmakingasaprephase?1457.2Onlineando(cid:15)ineexpressionreduction1457.3BTAandthetamingofself-application1537.4Arecipeforself-application1577.5Exercises159
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Context: 334ProgramAnalysisExercise15.4ApplytheclosureanalysistotheYcombinator.2Exercise15.5ApplytheclosureanalysistothelambdacalculusinterpreterinChapter8.2Exercise15.6Formonovariantbinding-timeanalysiswithoutpartiallystaticstruc-tures,thedomainofabstractvaluescontainingjustSandDistheobviouschoice.Explainwhythereisno‘obviouschoice’whenpartiallystaticstructuresalsomustbehandled.2
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Context: 142E(cid:14)ciency,Speedup,andOptimalityExercise6.31.UsethealgorithminSection6.3.3todeterminethespeedupintervalfortheTuringinterpreterinFigure4.4.2.WhatTuringprogramsbringtheactualspeedupsasclosetotheupperandlowerboundsaspossible?2Exercise6.4Theorem6.1placesanupperboundonthespeedupthatcanbeobtainedbypartialevaluation.Stateandproveatheoremaboutthelowerbound.Hint:A‘noslowdown’theorem.2Exercise6.51.IsTheorem6.1validforthepartialevaluatorforScheme0aspresentedinChapter5?Ifnot,cansupplementaryrestrictionsmakeitvalid?2.Samequestion,butforthe‘noslowdown’theoremfromExercise6.4.2Exercise6.6FormulatethealgorithminSection6.3.3forScheme0programs.2Exercise6.7FindanexampleprogramwhichshowsthatinDe(cid:12)nition6.2thecondition8">0:9k:8j>k:jp(sj;dj)jjpsj(dj)j2[u(cid:0)";v+"]ispreferabletothesimpler9k:8j>k:jp(sj;dj)jjpsj(dj)j2[u;v]2Exercise6.8InSection6.3.3itisstatedthatSU(l1)(cid:20)SU(l2)<1,implies:SU(l1)(cid:20)SU(l)=Cs(l)+Cd(l)Cd(l)(cid:20)SU(l2)Proveit.2Exercise6.9Ameta-interpretermintisaprogramthattakesasinput:aspeci(cid:12)ca-tionspecforalanguageS,anS-programp,andinputdatad.Thenmintreturnsthevalueofponinputd.1.Writethede(cid:12)ningequationformint.
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Context: Exercises1599.Programaspecializertobehaveasinstep4,inthelanguagebeingspecialized.10.Introspectonthewaytheannotationswereadded,anddeviseawaytodoasmuchaspossibleofitbymachine.11.Try[[mix]]mixpforvariousp.Warning:high-levelcontrolconstructssuchaswhileloops,patternmatching,andsomedeeplynestedconstructionscangiveproblems.Itisimportanttocutthelanguagedowntothebarebonesbeforebeginning,otherwisemuchtimewillbewastedonproblemsthatdistractattentionfromthehardcentralcoreoftheexercise,especiallysteps2to6.Inourexperience,manyoftheseproblemsareeasilyclearedupinretrospectafterthecoreisworking,andmanymorecanbedealtwithbyliberalusageof‘syntacticsugaring’and‘desugaring’,doneusingthecomputertotranslateintoandoutofarudimentarycorelanguage.FinalComment.Thisapproachprovidesaveryconvenientfactorizationoftheproblem:1.Whatshouldtheannotationsbe?Thisisaproblemofexpressiveness|theymustcontainsu(cid:14)cientinformationtobeabletodospecialization.Thesolutionisoftenatwo-levelsyntax,e.g.asusedinChapters4,5,8,andin[202]byNielsonandNielson.2.Howcanone(cid:12)ndappropriateannotations?Thisisaprogramanalysisprob-lem,solvablebyabstractinterpretationortypeinference.Consultthema-terialonbinding-timeanalysisfoundinthisbook.7.5ExercisesExercise7.1WriteasimpleonlinepartialevaluatorforScheme0.Compareittotheo(cid:15)inepartialevaluatorinChapter5inthefollowingareas:qualityofresidualprograms,partialevaluation-timee(cid:14)ciency,terminationproperties,andunfoldingstrategy.2Exercise7.2Writeasimpleonlinepartialevaluatorfor(cid:13)owcharts,asdoneinChapter4.Compareittotheo(cid:15)inepartialevaluatorinChapter4inthefollowingareas:qualityofresidualprograms,partialevaluation-timee(cid:14)ciency,terminationproperties,unfoldingstrategy.2Exercise7.3InSection7.2.3mixline,acompromisebetweeno(cid:15)ineandonline,issuggested.De(cid:12)nethedomainstobeusedbyamixlineexpressionreducerandthende(cid:12)nethemixlineexpressionreduceritself.2
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Context: Bibliography393[64]P.Cousot,‘Semanticfoundationsofprogramanalysis’,inS.S.MuchnickandN.D.Jones(eds.),ProgramFlowAnalysis:TheoryandApplications,chapter10,pp.303{342,Engle-woodCli(cid:11)s,NJ:PrenticeHall,1981.[65]P.CousotandR.Cousot,‘Abstractinterpretation:Auni(cid:12)edlatticemodelforstaticanalysisofprogramsbyconstructionorapproximationof(cid:12)xpoints’,inFourthACMSym-posiumonPrinciplesonProgrammingLanguages,LosAngeles,California,January1977,pp.238{252,NewYork:ACM,1977.[66]O.Danvy,‘Semantics-directedcompilationofnonlinearpatterns’,InformationProcessingLetters,37(6):315{322,March1991.[67]O.Danvy,‘Backtodirectstyle’,ScienceofComputerProgramming,1993.Toappear.[68]O.DanvyandA.Filinski,‘Representingcontrol:Astudyofthecpstransformation’,MathematicalStructuresinComputerScience,2(4):361{391,1992.[69]A.DeNiel,E.Bevers,andK.DeVlaminck,‘Partialevaluationofpolymorphicallytypedfunctionallanguages:Therepresentationproblem’,inM.Billaudetal.(eds.),AnalyseStatiqueenProgrammation(cid:19)Equationnelle,Fonctionnelle,etLogique,Bordeaux,France,Octobre1991(Bigre,vol.74),pp.90{97,Rennes:IRISA,1991.[70]H.Dybkj(cid:26)r,‘Parsersandpartialevaluation:Anexperiment’.StudentProject85-7-15,DIKU,UniversityofCopenhagen,Denmark,July1985.[71]H.Dybkj(cid:26)r,‘Categorytheory,types,andprogramminglanguages’,Ph.D.thesis,DIKU,UniversityofCopenhagen,Denmark,1991.AlsoDIKUReport91/11.[72]J.Earley,‘Ane(cid:14)cientcontext-freeparsingalgorithm’,CommunicationsoftheACM,13(2):94{102,February1970.[73]P.Emanuelson,‘Performanceenhancementinawell-structuredpatternmatcherthroughpartialevaluation’,Ph.D.thesis,Link(cid:127)opingUniversity,Sweden,1980.Link(cid:127)opingStudiesinScienceandTechnologyDissertations55.[74]P.Emanuelson,‘Fromabstractmodeltoe(cid:14)cientcompilationofpatterns’,inM.Dezani-CiancagliniandU.Montanari(eds.),InternationalSymposiumonProgramming,5thCol-loquium,Turin,Italy(LectureNotesinComputerScience,vol.137),pp.91{104,Berlin:Springer-Verlag,1982.[75]P.EmanuelsonandA.Haraldsson,‘Oncompilingembeddedlangu
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Context: 322ProgramAnalysisBd[[c]] (cid:28)(cid:14)‘=SBd[[x]] (cid:28)(cid:14)‘=SBd[[ife1e2e3]] (cid:28)(cid:14)‘=Bd[[e1]] (cid:28)(cid:14)‘tBd[[e2]] (cid:28)(cid:14)‘tBd[[e3]] (cid:28)(cid:14)‘Bd[[(callfie1...ea)]] (cid:28)(cid:14)‘=Faj=1Bdd[[ej]] (cid:28)(cid:14)‘((cid:28)xij)Bd[[(ope1...ea)]] (cid:28)(cid:14)‘=Faj=1Bd[[ej]] (cid:28)(cid:14)‘Bd[[((cid:21)x‘0.e)]] (cid:28)(cid:14)‘=Bdd[[e]] (cid:28)(cid:14)‘((cid:14)‘0)Bd[[(e1e2)]] (cid:28)(cid:14)‘=Bdd[[e1]] (cid:28)(cid:14)‘(Be[[e1]] (cid:28)(cid:14))tBdd[[e2]] (cid:28)(cid:14)‘twheret=Ff(cid:28)x‘j‘2Pe[[e1]]gBd[[(let(xe1)e)]] (cid:28)(cid:14)‘=Bdd[[e1]] (cid:28)(cid:14)‘((cid:28)x)tBd[[e2]] (cid:28)(cid:14)‘Bdd[[e]] (cid:28)(cid:14)‘t=Dift=Dand‘2Pe[[e]]=Bd[[e]] (cid:28)(cid:14)‘otherwiseFigure15.5:TheScheme1dynamiccontextfunctionBd.15.3.3ComparisonwiththerealSimilixThepresentbinding-timelatticeisfS;DgwithSasleastelement.Thebinding-timelatticeusedinSimilixisf?;S;Cl;Dg,whichdistinguishesstatic(cid:12)rst-ordervaluesfromstaticclosurevalues[27,Section5.7].Thenewbottomelement?,whichdescribesnon-terminatingexpressions,isneededbecausetheelementsSandClareincomparable.However,ClplaystheroleofatyperatherthanabindingtimeinSimilix.Itallowsthespecializertodistinguishstaticclosuresfromotherstaticdatawithoutusingtypetagsduringspecialization.ThusthedistinctionbetweenSandClisnotimportantforpurebinding-timereasonsandhasbeenleftouthere.Indeed,inabinding-timeanalysisforSimilixdevelopedrecently,thetypeandbinding-timeaspectshavebeenseparatedintotwodi(cid:11)erentanalyses.15.3.4Binding-timeanalysisofaScheme1programBinding-timeanalysisofaprogramshouldproduceasafedescription( ;(cid:28);(cid:14))whichisaspreciseaspossible.Thisisobtainedastheleastsolutiontoasetofequationsspecifyingthesafetyrequirement.First, mustmapalambdalabel‘tothebindingtimeBe[[e]] (cid:28)ofthebodyof(cid:21)x‘.e.Secondly,(cid:28)shouldmapafunctionvariablextoitsbindingtime,thatis,theleastupperbound(lub)ofthebindingtimesofthevaluesthatitcanbeboundto.ButthisisthelubofBv[[e]] (cid:28)(cid:14)xoverallfunctionbodiesbodyiintheprogram.Also,(cid:28)shouldmapalambdavariablex‘toitsbindingtime,thatis,thelubof
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Context: 384TheSelf-ApplicableScheme0Specializer(let((div1(polyupddiv0)))(if(equal?div0div1)div0(let((def(lookupfunfprogram)))(bv(bodydef)(vardef)sdpatdiv1)))))(sortdivision(finddivisionprogramsdpatternpolyupdate)));ReturnsSifeisstatic,Difeisdynamic.;vn=variablenames,vt=variablebindingtimes(define(beevnvt)(if(null?e)’S(if(number?e)’S(if(atom?e)(lookupbtevnvt)(if(equal?(tage)’quote)’S(if(equal?(tage)’if)(lub(be(e1e)vnvt)(lub(be(e2e)vnvt)(be(e3e)vnvt)))(if(equal?(tage)’call)(foldllub’S(map(lambda(e)(beevnvt))(callargse)))(if(equal?(tage)’op)(foldllub’S(map(lambda(e)(beevnvt))(callargse)))(error’be’"IllegalScheme0expression:~s"e)))))))))(define(lubt1t2)(if(equal?t1’D)’Dt2))(define(lub*t1st2s)(ift1s(cons(lub(cart1s)(cart2s))(lub*(cdrt1s)(cdrt2s)))()))(define(sdpattern-leqsdpat1sdpat2)(equal?sdpat2(lub*sdpat1sdpat2)))(define(sortdivisiondivision)(map(lambda(fun-sdpats)(cons(carfun-sdpats)(s0sortsdpattern-leq(cdrfun-sdpats))))division))(define(getsdpatternsfdivision)(let((binding(assocfdivision)))(ifbinding(cdrbinding)())))(define(lookupbtxxsvs)(ifxs(if(equal?x(carxs))(carvs)(lookupbtx(cdrxs)(cdrvs)))(error’lookupbt’"Unknownvariable:~s"x)))The(cid:12)leannotate.ssde(cid:12)nestheannotationfunctionsandoccurrencecountingfunctions.;File"annotate.ss"--Annotation(mono-orpolyvariant)ofScheme0programs
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Context: Conclusion201(cid:2)‘G1)(cid:2)1;...(cid:2)n(cid:2)1‘G2!G21...(cid:2)n‘G2!G2n(cid:2)‘G1;G2!G21;...;G2nifG1isstatic(cid:2)‘G1)(cid:15)(cid:2)‘G1;G2!failifG1isstatic(cid:2)‘G1!G01(cid:2)‘G2!G02(cid:2)‘G1;G2!G01;G02(cid:2)‘G!G0(cid:2)‘notG!notG0(cid:2)‘G1!G01(cid:2)‘G2!G02(cid:2)‘G1;G2!G01;G02(cid:2)‘G1)(cid:2)1;...(cid:2)n(cid:2)1‘G2!G21(cid:2)‘G1->G2;G3!G21ifG1isstatic(cid:2)‘G1)(cid:15)(cid:2)‘G3!G03(cid:2)‘G1->G2;G3!G03ifG1isstatic(cid:2)‘G1!G01(cid:2)‘G2!G02‘G3!G03(cid:2)‘G1->G2;G3!G01->G02;G03(cid:2)‘G1)(cid:2)1;...(cid:2)n(cid:2)1‘G2!G21...(cid:2)n‘G2!G2n(cid:2)‘if(G1;G2;G3)!G21;...;G2nifG1isstatic(cid:2)‘G1)(cid:15)(cid:2)‘G3!G03(cid:2)‘if(G1;G2;G3)!G03ifG1isstatic(cid:2)‘G1!G01(cid:2)‘G2!G02‘G3!G03(cid:2)‘if(G1;G2;G3)!if(G01;G02;G03)Figure9.6:SpecializationofProloggoals(partII).jobtime=sspeedupoutput=sint(sint;data)2:2513:7output=target(data)0:16target=mix(sintann;sint)19:21:78target=comp(sint)10:8comp=mix(mixann;sintann)19:31:35comp=cogen(sintann)14:4cogen=mix(mixann;mixann)172:01:14cogen=cogen(mixann)152:0
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Context: UsingtheScheme0specializer377Thee(cid:11)ectanduseofthemainfunctionsde(cid:12)nedbyloadingscheme0.ssanddoing(create)isdescribedbelow.TheprecisesyntaxofplainandannotatedScheme0programsisdescribedinSectionA.2.(monodivprogramsdpattern)Doesamonovariantbindingtimeanalysisofthesubjectprogramprogram,giventhebindingtimessdpatternofitsgoalfunction.Returnstheresulting(monovariant)division.(polydivprogramsdpattern)Similartomonodiv,butdoesapolyvariantbind-ingtimeanalysis.Returnstheresulting(polyvariant)division.(annotateprogramdivision)AnnotatestheScheme0programaccordingtothe(monovariantorpolyvariant)division.Maymakeseveralcopiesofeachfunctionforapolyvariantdivision.Returnstheannotated(two-level)Scheme0programorreportsthatdivisionisnotcongruent.(monotateprogramsdpattern)Doesmonovariantbindingtimeanalysisandan-notationofprogramgiventhebindingtimessdpatternofitsgoalfunction.Returnstheannotated(two-level)Scheme0program.(polytateprogramsdpattern)Similartomonotatebutperformspolyvariantbindingtimeanalysisandannotation.Returnstheannotated(two-level)Scheme0program.(specannprogramstaticinputs)Specializestheannotatedannprogramwithrespecttotheliststaticinputsofstaticargumentvalues.ReturnstheresidualScheme0program.(monopeprogramsdpatternstaticinputs)Doesamonovariantbindingtimeanalysisandannotationofprogram,thenspecializesitwithrespecttothegivenstaticinputsstaticinputs.ReturnstheresidualScheme0program.(polypeprogramsdpatternstaticinputs)Similartomonopebutdoespoly-variantbindingtimeanalysisandannotation.ReturnstheresidualScheme0program.(makefprogram)ConvertsprogramfromScheme0toSchemeandde(cid:12)nesaSchemefunctionftoinvoketheconvertedprogram.Returnsthenamef.Sidee(cid:11)ect:De(cid:12)nesfunctionf.Thisisforexecuting(residual)Scheme0programs.(schemeprogram)ConvertsprogramfromScheme0toScheme,possiblyrenam-ingfunctions.ReturnsalistofSchemefunctionde(cid:12)nitions.ThisisforstudyingresidualScheme0programs.onlineunfoldingGlobalBooleanvariable.If#t,thenannotategivesonthe(cid:13)ycallunfoldingasshow
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Context: 190PartialEvaluationfortheLambdaCalculusIftisofformt1!...!tn!D,theproofproceedsinaverysimilarmannerandweomitthecalculation.28.9ExercisesSomeoftheexercisesinvolve(cid:12)ndingaminimalcompletion.Theformalalgorithmtodothisistargetedforane(cid:14)cientimplementationandisnotsuitedtobeexecutedbyhand(forotherthanverysmallexamples).Soifnototherwisestatedjustusegoodsensefor(cid:12)ndingminimalcompletions.Exercise8.1Findaminimalcompletionforthelambdaexpressionlistedbelowgiventhebinding-timeassumptions(cid:28)=[m07!S,n07!D].Specializethepro-gramwithrespecttom0=42.((cid:21)m.(cid:21)n.+mn)m0n02Exercise8.2Findaminimalcompletionforthelambdaexpressionlistedbelowgiventhebinding-timeassumptions(cid:28)=[x07!S,xs07!S,vs07!D].Specializetheprogramwithrespecttox0=candxs0=(abcd).(fix(cid:21)lookup.(cid:21)x.(cid:21)xs.(cid:21)vs.if(null?xs)then’errorelseif(equal?x(carxs))then(carvs)else(lookupx(cdrxs)(cdrvs)))x0xs0vs02Exercise8.3Inpreviouschapters,aself-interpretersinthasbeende(cid:12)nedby[[sint]]Lpd=[[p]]LdDe(cid:12)nesint,basingitonEforinstance,suchthatthisequationholdsforthelambdacalculus.2Exercise8.41.Writeaself-interpretersintforthelambdacalculusbytransformingthefunctionEintoalambdaexpressionfix(cid:21)E.(cid:21)e.(cid:21)(cid:26).if...e’(cid:26)’withfreevariablese’and(cid:26)’.2.Findaminimalcompletionforsintgivenbinding-timeassumptions(cid:28)=[env’7!S,(cid:26)’7!D].3.Findaminimalcompletionforsintgivenbinding-timeassumptions(cid:28)=[env’7!S,(cid:26)’7!(S!D)].
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Context: 100PartialEvaluationforaFlowChartLanguage2Exercise4.4Considerthemix-generatedprograminFigure4.5.Theprogramissuboptimalintwoways;discusshowtorevisethepartialevaluationstrategytoobtainanoptimalresidualprograminthisparticularexample.1.Thesameconditionalstatementappearstwice.2.TheassignmentstothevariableLeftdonotcontributetothe(cid:12)nalanswer.Wouldtheproposedrevisionshaveanyadversee(cid:11)ects?2Exercise4.5SpecializetheTuringinterpreterwithrespecttotofollowingprogram:0:ifBgoto31:right2:goto03:write14:ifBgoto75:left6:goto47:write12Exercise4.6ThemixequationandtheFutamuraprojectionsaspresentedinthischapterglossoverthefactthatpartialevaluation(here)consistsofabinding-timeanalysisphaseandaspecializationphase.Re(cid:12)nethemix-equationandtheFutamuraprojectionstore(cid:13)ectthistwo-phaseapproach.2Exercise4.7WillspecializationoftheTuringinterpreter(Figure4.4)withrespectaprogrampterminateforallTuringprogramsp?2Exercise4.8UsethealgorithminSection4.4.6todetermineacongruentdivisionfortheTuringinterpreterwhenthedivisionfortheinputvariables(Q,Right)is1.(S;D),2.(D;S).2Exercise4.9Writeabindingtimeanalysisalgorithmthatcomputesapointwisedivision.2Exercise4.10Writeabindingtimeanalysisalgorithmthatcomputesapolyvariantdivision.2Exercise4.11ThebindingtimeanalysisalgorithmfromSection4.4.6isnotlikelytobeverye(cid:14)cientinpractice.Constructane(cid:14)cientalgorithmtodothesamejob.2
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Context: 10Chapter1  (cid:127)  Introduction to Intrusion Detection and SnortIdeally a honey pot should look like a real system. You should create some fakedata files, user accounts and so on to ensure a hacker that this is a real system. This willtempt the hacker to remain on the honey pot for a longer time and you will be able torecord more activity.To have more information and get a closer look at honey pots, go to the Honey PotProject web site http://project.honeynet.org/ where you will find interesting material.Also go to the Honeyd web site at http://www.citi.umich.edu/u/provos/honeyd/ to findout information about this open source honey pot. Some other places where you canfind more information are:(cid:127)South Florida Honeynet Project at http://www.sfhn.net(cid:127)Different HOWTOs at http://www.sfhn.net/whites/howtos.html1.1.4Security Zones and Levels of TrustSome time ago people divided networks into two broad areas, secure area andunsecure area. Sometimes this division also meant a network is inside a firewall or arouter and outside your router. Now typical networks are divided into many differentareas and each area may have a different level of security policy and level of trust. Forexample, a company’s finance department may have a very high security level and mayallow only a few services to operate in that area. No Internet service may be availablefrom the finance department. However a DMZ or de-militarized zone part of your net-work may be open to the Internet world and may have a very different level of trust.Depending upon the level of trust and your security policy, you should also havedifferent policies and rules for intruder detection in different areas of your network.Network segments with different security requirements and trust levels are kept physi-cally separate from each other. You can install one intrusion detection system in eachzone with different types of rules to detect suspicious network activity. As an example,if your finance department has no web server, any traffic going to port 80 in the financedepartment segment may come under scrutiny for intruder activity. The same is not truein the DMZ zone where you are running a company web server accessible to everyone.1.2IDS PolicyBefore you install the intrusion detection system on your network, you must have a pol-icy to detect intruders and take action when you find such activity. A policy must dictateIDS rules and how they will be applied. The IDS policy should contain the followingcomponents; you can add more depending upon your requirements.
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Context: 98PartialEvaluationforaFlowChartLanguage4.11SummaryandamoreabstractperspectiveThischapterdiscussedself-applicablepartialevaluationofa(cid:13)owchartlanguage.Inspiteofthesimplicityofthislanguage,weshallseethatalmostalltheconceptsintroducedherearealsocentraltopartialevaluationofmorecomplexlanguages:functionallanguages,logicprogramminglanguages,imperativelanguageswithre-cursiveprocedures,etc.Furthermore,essentiallythesamemethodssu(cid:14)ceforpartialevaluationofsuchseeminglyratherdi(cid:11)erentlanguages.TheessenceofprogramspecializationTheprevioussectionswereintentionallyconcreteanddetailed.Wenowreducetheideasinvolvedtotheiressentialcore,asapreliminarytopartialevaluationofmorecomplexlanguages.Almostallprogramminglanguagesinvolvesomeformofstate,whichmaybeapair(pp,store)asinthe(cid:13)owchartlanguage;or(fname,environment)inafunc-tionallanguage,wherefnameisthenameofthefunctioncurrentlybeingevaluatedandenvironmentbindsactualparameterstotheirvalues;or(pname,argumentlist)inPrologwherepnameisthenameofthecurrentprocedure(predicate)andargu-mentlistisalistofterms,perhapscontaininguninstantiated(free)variables.Equallycentralistheconceptofastatetransition,e(cid:11)ectedbyagoto,afunctioncall,orapredicatecall.Eachchangesthestateandcontrolpoint,describedsymbolicallyby(pp;v))(pp0;v0);wherep;p0arecontrolpointsandv;v0aredatasuchasstoresorargumentlists.Supposethereisawaytodecomposeorfactoradatavaluevintostaticanddynamicpartswithoutlossofinformation.Suchadatadivisioncanbethoughtofasatripleoffunctions(stat;dyn;pair),eachmappingthesetVofdatavaluestoitself.Theabilitytodeomposeandrecomposewithoutinformationlosscanbeexpressedbythreeequations:pair(stat(v);dyn(v))=vstat(pair(vs;vd))=vsdyn(pair(vs;vd))=vdRemarks.Inthischapteradivisionwasspeci(cid:12)edbyanS(cid:0)Dvector,forinstanceSDSDspeci(cid:12)edthedivisionofV=D4wherepair((a;c);(b;d))=(a;b;c;d),stat((a;b;c;d))=(a;c),anddyn((a;b;c;d))=(b;d).Moregenerally,o(cid:15)inespe-cializersasusedinthischapterwilluseasingle,prede(cid:12)neddivision,wher
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Context: Compilingcompilers593.Uset10toobtainanS0compilert20intargetlanguageform:S0TT-S0TT-S0TS-high-levelcompilerh023low-levelcompilert20low-levelcompilert1024.Finally,uset20toobtainanS0compilert30intargetlanguageform:S0TT-S0TT-S0TS-high-levelcompilerh023low-levelcompilert30low-levelcompilert202TheserunscanbewrittenmoreconciselyinthenotationforprogramexecutionintroducedinChapter1:t10=[[t]]Th0t20=[[t10]]Th0t30=[[t20]]Th0Nowt10andt20(andt30)aresemanticallyequivalentsincetheyareallobtainedfromthesamesourceprogram,h0.[[t10]]T=[[[[h]]Sh0]]Tbyde(cid:12)nitionoft10=[[[[h0]]Sh0]]Tsinceh0isaconservativeextensionofh=[[[[[[t]]Th0]]Th0]]TsincetisacompilerfromStoT=[[[[t10]]Th0]]Tbyde(cid:12)nitionoft10=[[t20]]Tbyde(cid:12)nitionoft20Notethatt10andt20maynotbetextuallyidenticalsincetheywereproducedbytwodi(cid:11)erentcompilers,tandt10,anditisquitepossiblethattheextendedlanguageS0mayrequiredi(cid:11)erenttargetcodethanS.However,itshouldbeclearthatt20andt30aretextuallyidenticalsincethecompilersusedtocompilethemaresemanticallyequivalent|atleast,ifoneassumestheextendedcompilerh0tobecorrect.Iftheyarenottextuallyidentical,thenh0(or,lesslikely,t)mustbewrong.Notethatbootstrappinginvolvesself-applicationinthesensethat(compiledversionsof)h0areusedtocompileh0itself.Moreover,self-applicationisuseful:itgivesasimplewaytoproveh0wrong.
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Context: 176PartialEvaluationfortheLambdaCalculuseasilycomputed.ThedescriptionisadaptedfromapaperbyFritzHenglein[114].Thatpaperalsogivesthedetailsofthee(cid:14)cientconstraintreductionandproofsofthestatedpropositions.Thede(cid:12)nitionofcompletionsplacesnorestrictionontheinsertionoflifts(apartfromtheobviousdemandthattheexpressionmustbewell-annotated).Tosimplifytheexposition,weshallassumefornowthatnoliftsareallowed.Oncethebasicconceptsaretreatedwepresentthemodi(cid:12)cationsneededtoaccountforlift-insertion(Section8.7.3).8.7.1CombiningstaticanddynamictyperulesConsiderthetyperulesfor(cid:21)and(cid:21):(Abstr)(cid:28)[x7!t2]‘te:t1(cid:28)‘(cid:21)x.te:t2!t1(Abstr-dyn)(cid:28)[x7!D]‘te:D(cid:28)‘(cid:21)x.te:DComparetheseruleswithacombinedrule,whichwecallAbstr-comb:(Abstr-comb)(cid:28)[x7!t2]‘e:t1((t2!t1)=t_t=t1=t2=D)(cid:28)‘(cid:21)x.e:tAnapplicationofrule(Abstr-comb)correspondsexactlytoanapplicationofeither(Abstr)or(Abstr-dyn),dependingonwhichdisjunctsholdin(t2!t1=t_t=t1=t2=D).Bymakingcombinedrulesfortheotheroperatorsthathavebothstaticanddynamicversionswegetatypesystem,TypeComb,inwhichanexpressioneistypableifandonlyifehascompletions.GiventhetypeofallsubexpressionsofeinTypeComb,weimmediatelyhavethecorrespondingcompletionofe.Forexample,ifasubexpressionofform(cid:21)x.e1hastypeD,theninthecorrespondingcompletionthelambdawillbeannotated(cid:21).We(cid:12)ndcompletionsforanexpressioneby(cid:12)ndingtypingsofeinTypeComb.8.7.2ConstraintsonexpressiontypesLetebea(cid:21)-expressionand(cid:28)abinding-timeenvironmentfore.Weassociateauniquetypevariable(cid:11)xwithevery(cid:21)-boundvariablexoccurringineandauniquetypevariable(cid:11)e1witheverysubexpressione1ine.Weassumethatall(cid:21)-boundvariablesaredistinct.Asaninformalexampleconsidertheexpression((cid:21)x.xy)zandassumebinding-timeenvironment(cid:28)=[y7!D,z7!D].Let(cid:11)bethetypevariableassociatedwith((cid:21)x.xy).AllotherexpressionsmusthavetypeD,butboth(cid:11)=Dand(cid:11)=D!Dwouldgivecorrecttypings.Thecorrespondingcompletio
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Context: 156Online,O(cid:15)ine,andSelf-application7.3.3RemovinggeneralitybyannotationsBelowmix1andmix2mayormaynotbeidentical.Theindexesareintroducedforeasierreferenceonly.Duringcompilergenerationcomp=[[mix1]]L[mix2,int]mix2ispartiallyevaluatedwithrespecttoincompleteinput,int.Recallthatmix2constantlytestswhetherthepartialvaluesareconstantsinVal(...)orresidualexpressionsinExp(...),andthatthesetestsaredynamicandappearinthegeneratedcompiler.Thisisintuitivelywrongbecauseitshouldbeknownatcompilergenerationtimethatthesyntacticdispatchintheinterpreterwillbestaticatcompilationtime.Tomakethesetestsstaticinmix2,extrainformationmustbesuppliedbecausethereisnowayapriorythatmix2caninferthatcompshouldproduceonlynormaltargetprogramsandnocrazyprogramsasshownabove.Theinterpreterinthastwoarguments,sourceanddata,andmix2mustknowthatthecompilerisonlyintendedforapplicationtosource.Oneconvenientwaytocommunicatethisinformationtomix2istoannotatetheactionsofintaseitherstaticordynamic.Thismakesthebindingtimesofintapparentassyntax,2andthesyntacticdispatchinmix2iscertainlyreducedbymix1.Theclaimisthusthatmix2shouldbeano(cid:15)inepartialevaluatorandthataccordinglyintshouldbeannotated.Intheannotatedinterpreter,intann,theannotatedversionofthesubexpressionEconsideredabove,wouldbe:(consd(lift(carsnames))(cardvalues))RecallfromthebeginningofSection7.2thattheOff-ValueshandledbyOffPEareuntagged.Itisthecorrectnessoftheannotationsensuresthatlookupreturnsaconstantvaluewhenappliedtonamesandaresidualexpressionwhenappliedtonames.Letoffmixbeano(cid:15)inepartialevaluatorwhichusesOffPEtoreduceexpres-sions.Considergenerationofacompilerfromtheannotatedinterpreterintannbycomputing[[offmix]]L[offmixann,intann].Thedecisionsinoffmixannwhethertodoevaluationortogeneratecodewillnotdependonoffmixann’sunavailableinput(thesourceprogramsource),butonlyontheannotationsintheinterpreter.Thusthedecisioncanbemadewhenthecompilerisgeneratedandneednotbemadeaneweverytimethecompilerisappliedtoasourceprogram.AfragmentofthecompilerisgiveninFigur
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Context: Contentsix17.4Supercompilationanddeforestation35817.5Exercises36418GuidetotheLiterature36618.1Abriefhistoricaloverview36618.2Partialevaluationliteraturebysubjectlanguage36818.3Principlesandtechniques37018.4Applications37218.5Othertopicsrelatedtopartialevaluation374ATheSelf-ApplicableScheme0Specializer376A.1UsingtheScheme0specializer376A.2DatastructuresintheScheme0specializer378A.3ThecomponentsoftheScheme0specializer380Bibliography389Index406
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Context: 90Chapter3  (cid:127)  Working with Snort RulesThe name is a name used for the classification. The name is used with theclasstype keyword in Snort rules. The description is a short description of the classtype. Priority is a number that shows the default priority of the classification, which canbe modified using a priority keyword inside the rule options. You can also place theselines in snort.conf file as well. An example of this configuration parameter is asfollows:config classification: DoS,Denial of Service Attack,2In the above line the classification is DoS and the priority is 2. In Chapter 6, youwill see that classifications are used in ACID,2 which is a web-based tool to analyzeSnort alert data. Now let us use this classification in a rule. The following rule usesdefault priority with the classification DoS:alert udp any any -> 192.168.1.0/24 6838 (msg:"DoS"; \   content: "server";  classtype:DoS;)The following is the same rule but we override the default priority used for theclassification.alert udp any any -> 192.168.1.0/24 6838 (msg:"DoS"; \   content: "server";  classtype:DoS; priority:1)Using classifications and priorities for rules and alerts, you can distinguishbetween high- and low-risk alerts. This feature is very useful when you want to escalatehigh-risk alerts or want to pay attention to them first.N O T E Low priority numbers show high priority alerts.If you look at the ACID browser window, as discussed in Chapter 6, you will seethe classification screens as shown in Figure 3-3. The second column in the middle partof the screen displays different classifications for captured data.Other tools also use the classification keyword to prioritize intrusion detectiondata. A typical classification.config file is shown below. This file is distrib-uted with the Snort 1.9.0. You can add your own classifications to this file and use themin your own rules.2.ACID stands for Analysis Control for Intrusion Detection. It provides a web-based user interface to analyze data generated by Snort.
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Context: Exercises99((pp;vs);vd))((pp0;v0s);v0d)Thisisalsoatransition,butonewithspecializedcontrolpoints(pp;vs)and(pp0;v0s),eachincorporatingsomestaticdata.Theruntimedataarevd,v0d,theresultofthedynamicprojections.FundamentalconceptsrevisitedResidualcodegenerationamountsto(cid:12)ndingcommandsorafunctionorprocedurecallwhichsyntacticallyspeci(cid:12)esthetransitionfromvdtov0d.Ifvd=v0dthentransitioncompressionmaybepossiblesincenoresidualcodebeyondatmostacontroltransferneedbegenerated.(For(cid:13)owcharts,thishappensifthebasicblockbegunbyppcontainsnodynamicexpressionsorcommands.)Finally,wehaveseenthecongruenceconditiontobeneededforcodegeneration.Inthecurrentcontextthisbecomes:v0smustbefunctionallydeterminedbyvsineverytransition.4.12ExercisesExercise4.1Writeaprogramandchooseadivisionsuchthatpartialevaluationwithouttransitioncompressionterminates,andpartialevaluationwithtransitioncompressiononthe(cid:13)y(asdescribedinthischapter)loops.2Exercise4.2Thepurposeofthisexerciseistoinvestigatehowmuchcertainex-tensionstothe(cid:13)owchartlanguagewouldcomplicatepartialevaluation.Foreachconstruction,analysepossibleproblemsandshowthespecializationtimecompu-tationsandthecodegeneration1.forloop,2.whileloop,3.case/switchconditional,4.computedgoto,cgotohExpri,whereExprevaluatestoanaturalnumber=alabel,5.gosub...return.Doanyoftheaboveconstructionscomplicatethebinding-timeanalysis?2Exercise4.3AttheendofSection4.2.2itismentionedthatmixcouldgenerateresidualprogramsinalow-levellanguage,e.g.machinecode.1.Writethemixequationforamixthatgeneratesmachinecode.2.DotheFutamuraprojectionsstillhold?3.Whataretheconsequencesforcompilergeneration?
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Context: 182PartialEvaluationfortheLambdaCalculusProof1.De(cid:12)neamegastepasfollows:applyanyapplicableruleandthenapplytheequationalconstrainttransformationrulesexhaustively.Itiseasytoseethateverymegastepterminatesandthatafteritterminatesallequationalconstraintshavebeeneliminated.Letcbethenumberofconstraints;nthenumberofvariablesoccurringinthem;andvthenumberofinequalityconstraintswithavariableontheleft-handside.Itiseasytocheckthateverymegastepdecreasesthesumc+n+2vbyatleastone.Consequentlyeverysequenceofmegastepsterminates.2.Byde(cid:12)nitionofnormalform.3.Noneoftherulesintroduce(cid:20)b-inequalitieswithavariableontheleft-handside.2FromaconstraintsystemCinnormalformwecan(cid:12)ndasolutionthatcharacter-izesaminimalcompletionoftheoriginalexpressione.Considertwocompletionste1andte2.Ifanoperatorisstaticinte1anddynamicinte2,thentherewillbeatleastonesubexpressionoftypeDinte2,wherethecorrespondingsubexpressioninte1hasatypet6=D.NowletCbeanormalformconstraintsystem.ThenormalizationprocesscanhaveforcedsubexpressionstohavetypeD,butthesesubexpressionsmustbedynamicinallcompletions(cf.Proposition8.4).ToensurethatnoothersubexpressionsgettypeD,weinterpretallinequalitiesinCasequations.SinceCisanormalformconstraintsystem,byProposition8.5,part2,theseequationshaveamostgeneraluni(cid:12)erU[163].LetBSbethesubstitutionthatmapseverytypevariableoccurringinU(C)toS.SinceneitherUnorBSsubstitutesDforanytypevariable,allthedependencyconstraintsinCaretriviallysatis(cid:12)ed.Notethattheremaybeotherwaysofchoosingaminimalcompletion,sincesomesubstitutionsofDforatypevariablecorrespondtomovingalift-expression(seeSection8.4).AvoidingtypeDwheneverpossibleisasimplewaytochooseoneminimalcompletion.8.7.5Binding-timeanalysisalgorithmGivenanexpressioneandabinding-timeassumption(cid:28),dothefollowing:1.GeneratetheconstraintsystemC=C(cid:28)(e)(Section8.7.3).2.ComputenormalformconstraintsystemCnfsuchthatCV)+Cnf(Sec-tion8.7.4).
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Context: :12)-nitestatetransducerscanbecombinedintoonewithnointermediatesymbols;deforestation,seeninChapter17;andcompositionofderivorsorofattributecou-pledgrammars.Thelattertwocanbeusedautomaticallytocombinemultipasscompilerphasesintoasinglephase.
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Context: 76Chapter3  •  Working with Snort Rulesare usually placed in a configuration file, typically snort.conf. Youcan also use multiple files by including them in a main configuration file.This chapter provides information about different types of rules as well asthe basic structure of a rule. You will find many examples of commonrules for intrusion detection activity at the end of this chapter. After read-ing this chapter, along with the two preceding chapters, you should haveenough information to set up Snort as a basic intrusion detection system.3.1TCP/IP Network LayersBefore you move to writing rules, let us have a brief discussion about TCP/IP layers.This is important because Snort rules are applied on different protocols in these layers.TCP/IP is a five layer protocol. These layers interact with each other to make thecommunication process work. The names of these layers are: 1.The physical layer.2.The data link layer. In some literature this is also called the network interfacelayer. The physical and data link layers consist of physical media, the networkinterface adapter, and the driver for the network interface adapter. Ethernetaddresses are assigned in the data link layer.3.The network layer, which is actually IP (Internet Protocol) layer. This layer isresponsible for point-to-point data communication and data integrity. All hostson this layer are distinguished by IP addresses. In addition to IP protocol,ICMP (Internet Control Message Protocol) is another major protocol in thislayer. Information about IP protocol is available in RFC 791 available at http://www.rfc-editor.org/rfc/rfc791.txt. Information about ICMP protocol is avail-able at http://www.rfc-editor.org/rfc/rfc792.txt.4.The transport layer, which is actually TCP/UDP layer in the TCP/IP protocol.TCP (Transmission Control Protocol) is used for connection-oriented and reli-able data transfer from source to destination. UDP (User Datagram Protocol),on the other hand, is used for connectionless data transfer. There is no assur-ance that data sent through UDP protocol will actually reach its destination.UDP is used where data loss can be tolerated. Information about UDP protocolis available in RFC 768 at http://www.rfc-editor.org/rfc/rfc768.txt. Informationabout TCP protocol is available in RFC 793 at http://www.rfc-editor.org/rfc/rfc793.txt.
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Context: 382TheSelf-ApplicableScheme0Specializer));Generalauxiliaryfunctions(define(foldlfabs)(ifbs(foldlf(fa(carbs))(cdrbs))a))(define(foldrfabs)(ifbs(f(carbs)(foldrfa(cdrbs)))a))(define(allbs)(ifbs(and(carbs)(all(cdrbs)))’t))(define(s0sortleq?xs)(define(insertxxs)(ifxs(if(leq?x(carxs))(consxxs)(cons(carxs)(insertx(cdrxs))))(listx)))(foldrinsert()xs))(define(number->stringn);Worksonlyforstrictlypositiven(define(num->strndigits)(if(equal?n0)digits(num->str(quotientn10)(cons(integer->char(+48(remaindern10)))digits))))(list->string(num->strn())));***EvalisnotstandardScheme.RedefinetosuityourSchemeversion***;ForChezScheme,xscheme,TIPCScheme,andelk:(define(s0evalschemeexpression)(evalschemeexpression));ForYaleTScheme:;(define(s0evalschemeexpression)(evalschemeexpressionscheme-env));Mainvariablesandfunctionsforexperiments(defineonlineunfolding’t)(define(create)(maps0eval(mapsugartoschemesugars))(load"spec.ss")(make’specspecializer)(load"annotate.ss")(load"analyse.ss")(load"subject.ss"))(define(polytateprogramsdpat)(annotateprogram(polydivprogramsdpat)))(define(monotateprogramsdpat)(annotateprogram(monodivprogramsdpat)))(define(polypeprogramsdpatvs0)(specialize(polytateprogramsdpat)vs0))(define(monopeprogramsdpatvs0)(specialize(monotateprogramsdpat)vs0))(define(makenamresidual)(let((schemeprogram(schemeresidual)))(let((f(name(carschemeprogram))))(s0eval(cons’define(cons(consnam’args)(appendschemeprogram(list(list’applyf’args)))))))))The(cid:12)leanalyse.ssde(cid:12)nesthebindingtimeanalysisfunctions,andfunctionsforhandlingdivisionsandsdpatterns.
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Context: Installing Snort39present in the /var/rules directory. You can keep all rule files and snort.conffile in the same directory if you set the value of this variable to ./ instead of ../rules in the snort.conf file using the following line: var RULE_PATH ./More information about Snort rules is found in the next chapter where you willlearn how to define your own rules as well.The classification.config file contains information about Snort rulesclassification and more information about this file is found in the next chapter. Note that/opt/snort-1.9.0 is the directory where all Snort source code files are present. Ifyou are using a different version of Snort, the directory name will be different.The reference.config file lists URLs for different reference web siteswhere more information can be found for alerts. These references are used in Snortrules and you will learn more about references in the next chapter. A typical refer-ence.config file is like the following:# $Id: reference.config,v 1.3 2002/08/28 14:19:15 chrisgreen Exp $# The following defines URLs for the references found in the rules## config reference: system URLconfig reference: bugtraq   http://www.securityfocus.com/bid/ config reference: cve       http://cve.mitre.org/cgi-bin/cvename.cgi?name=config reference: arachNIDS http://www.whitehats.com/info/IDS# Note, this one needs a suffix as well.... lets add that in a bit.config reference: McAfee    http://vil.nai.com/vil/content/v_config reference: nessus    http://cgi.nessus.org/plugins/dump.php3?id=config reference: url       http://Note that both classification.config and reference.config filesare included in the main snort.conf file.N O T E If you used the RPM package, all configuration files are already present inthe /etc/snort directory and you don’t need to take the above mentioned actions.
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Context: Index407Augustsson,L.,359B(booleans),26Bd(Scheme1dynamiccontext),322Bdd(Scheme1dynamiccontext),322Be(Scheme0binding-timeanalysis),108Be(Scheme1binding-timeanalysis),321Bpe(PELbinding-timeanalysis),330Bpv(PELbinding-timepropagation),331Bv(Scheme0binding-timepropagation),109Bv(Scheme1binding-timepropagation),321backwardsuni(cid:12)cation,197Barendregt,H.P.,45basiccon(cid:12)guration,355,369Beckman,L.,358,367,368Berlin,A.,283,287,374Berry,D.M.,374bindingtimesmixed,149,153,271polyvariant,112binding-timeannotations,194assumption,172debugger,151engineering,2environment,320improvement,92,198,218,263,371binding-timeanalysis,83,370byconstraintsolving,175e(cid:14)cient,175ensuringtermination,304forC,253forlambdacalculus,182forPEL,328forScheme0,106,313forScheme1,222,319forSimilix,322monovariant,106projection-based,328safetyof,106BindingTime(setofbindingtimes),107Bird,R.,374birth-placeanalysis,244Bj(cid:28)rner,D.,367blazing,362bodyoffunction,44,102Bondorf,A.,150,163,194,195,204,218,267,368{372booleansB,26bootstrapping,58bottom(?),24,311,323boundoccurrence,45boundedstaticvariation,116,266,299Boyer,R.S.,374Bruynooghe,M.,371BTA,seebinding-timeanalysisbubblesort,289Bulyonkov,M.A.,368Bundy,A.,373Burstall,R.M.,347,350,374Cprogramminglanguage,229,368C-mix,257callunfolding,371call-by-name,48,53,349call-by-value,48,53,349recursionequations,53call-graphanalysis,253capture,47car,33Carlsson,M.,373Cartesianproduct,23,26cdr,33CERES,375Chin,W.N.,362Christiansen,H.,375Church,A.,43Church{Rossertheorem,48Church-Turingthesis,335circuitsimulation,283closedexpression,52closure,52,209closureanalysis,315closureanalysisfunction,316closurepropagationfunction,316codeduplication,119codegeneration,79codinginonlinepartialevaluation,147Codish,M.,373commutativelaw,263compiler,8,39e(cid:14)cient,156generatedbyo(cid:15)inePE,156generatedbyonlinePE,154generationof,13,86overlygeneral,154structureofgenerated,89typeof,337,343compilergenerator,375generationof,14semantics-directed,375typeof,345complementofprojectio
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Context: Contentsix3.5Rule Headers 813.5.1Rule Actions 813.5.2Protocols 833.5.3Address 843.5.4Port Number 863.5.5Direction 883.6Rule Options 883.6.1The ack Keyword 893.6.2The classtype Keyword 893.6.3The content Keyword 933.6.4The offset Keyword 943.6.5The depth Keyword 953.6.6The content-list Keyword 953.6.7The dsize Keyword 953.6.8The flags Keyword 963.6.9The fragbits Keyword 973.6.10The icmp_id Keyword 983.6.11The icmp_seq Keyword 983.6.12The itype Keyword 983.6.13The icode Keyword 993.6.14The id Keyword 1003.6.15The ipopts Keyword 1003.6.16The ip_proto Keyword 1013.6.17The logto Keyword 1023.6.18The msg Keyword 1033.6.19The nocase Keyword 1033.6.20The priority Keyword 1033.6.21The react Keyword 1043.6.22The reference Keyword 1043.6.23The resp Keyword 1053.6.24The rev Keyword 1073.6.25The rpc Keyword 1073.6.26The sameip Keyword 1083.6.27The seq Keyword 1083.6.28The flow Keyword 1083.6.29The session Keyword 1093.6.30The sid Keyword 1103.6.31The tag Keyword 1103.6.32The tos Keyword 1113.6.33The ttl Keyword 111
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Context: AnoverviewofSimilix20710.1.3Two-levelScheme1expressionsAsusualpartialevaluationisdoneinphases.Thepreprocessingphaseyieldsanannotatedtwo-levelScheme1programwhichisthensubmittedtothespecializationphase.TheannotationsfollowthepatternfromChapters5and8inthateveryoperatorif,call,hOpi,lambda,application,andlet,comesinastaticandady-namicversioninthetwo-levelScheme1syntax.Inadditionithaslift-expressions(Figure10.2).hExpri::=hConstantiConstantjhVariVariablej(ifshExprihExprihExpri)Staticconditionalj(ifdhExprihExprihExpri)Dynamicconditionalj(callshFuncNameihSDArgsi)Staticfunctionappl.j(calldhFuncNameihSDArgsi)Dynamicfunctionappl.j(hOpishExpri...hExpri)Staticbaseappl.j(hOpidhExpri...hExpri)Dynamicbaseappl.j(lifthExpri)Liftingastaticexpr.j(lambdas‘(hVari)hExpri)Staticlambdaj(lambdad‘(hVari)hExpri)Dynamiclambdaj(hExprihExpri)Staticlambdaapplic.j(hExpri@dhExpri)Dynamiclambdaappl.j(lets(hVarihExpri)hExpri)Staticlet-bindingj(letd(hVarihExpri)hExpri)Dynamiclet-bindinghSDArgsi::=(hArglisti)(hArglisti)ArgumentlistsFigure10.2:Syntaxoftwo-levelScheme1expressions.10.1.4Binding-timeanalysisforScheme1TheScheme1binding-timeanalysisisanextensionoftheScheme0binding-timeanalysisBegiveninSection5.2.However,therearethreenewproblems:(1)higher-orderapplications(e0e1),(2)staticlambdaabstractionsindynamiccontexts,and(3)let-expressions.(1)Higher-orderapplicationsWhenanalysingahigher-orderapplication(e0e1)itisusefultoknowwhichfunctionisapplied,ormoregenerally,whichfunctionsmaybeapplied.TheclosureanalysispresentedinSection15.2providessuchinformation:thepossiblevaluesofe0.Usingthisinformation,binding-timeanalysisofhigher-orderapplicationsisbasicallyrathersimilartothatof(cid:12)rst-orderapplications.Thebinding-timeanalysiswillbepresentedinSection15.3,aftertheclosureanalysis.
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Context: 94PartialEvaluationforaFlowChartLanguagepending.Theresultisstoredinblocks-in-pending.Whenmixtakesaprogrampointppfrompendingandlooksitup,itdoesnotscanthewholeprogram,onlyblocks-in-pending.(Romanenkogivesthe(cid:12)rstdescriptionofthisidea[227]).Thereadermightwanttore-examinetheinterpreterinFigure4.4andthestructureofthegeneratedcompilerinFigure4.8toseethatthegeneratedcase-statementcontainsonebranchforeachprogrampointamonginit,cont,jumpthatiseitherinitial(init)orthetargetofaconditionaljumpwithadynamiccondition(cont,jump).Theunderlyingreasonwhyonlythreeoftheinterpreter’s15labelscontributetothecase-statementisthattransitioncompressingisdoneonthe(cid:13)y.Ifmixhadgeneratedallthetrivialgotos,andcompressedthemafterwards,manyspecializedprogrampoints,namelythetargetsofallgotos(andnotonlythetargetsofresidualconditionals),wouldbeaddedtopendingduringprogramspecialization.Thiswouldmeanthateveryprogrampointpp0,beingthetargetofagotointhesubjectprogram,wouldbeinthesetfpp0,...,ppng.Thisadvantageofcompressingonthe(cid:13)yiscertainlynotevidentat(cid:12)rstsight:smallerresidualprogramsareproducedwhenmixisself-applied.4.9Thegranularityofbinding-timeanalysisUntilnowwehaveassumedthattaskofbinding-timeanalysisistocomputeonedivision,validatallprogrampoints.Formostsmallprogramsithasturnedouttobeaveryreasonableassumptionthatonedivisioncouldbeusedtoclassifythestatusofavariablethroughoutthewholeprogram.Thereare,however,aseriesofobjectionstothissimpli(cid:12)edviewofBTA.4.9.1PointwisedivisionsConsiderthefollowingprogramfragmentwheretheinitialdivisionis(S;D):read(XY);init:X:=X+1;Y:=Y-1;gotocont;cont:Y:=3;next:...Obviouslyacongruent,uniformdivisionwouldhavetobe(S;D),but(judgingfromtheshownfragmentalone)amoredetaileddivisioninit:(S;D),cont:(S;D),next:(S;S)wouldbesafe.Weshallcallsuchadivisionpointwise.Asopposedtothesimplestpossiblebinding-timeanalysiscomputinguniformdivisions(Section4.4.6),ananalysistocomputepointwisedivisionswillhavetoconsiderthecontrol(cid:13)owoftheprogram.Constructingthealgorith
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Context: viiiContentsIVPartialEvaluationinPractice26112Binding-TimeImprovements26312.1Acasestudy:Knuth,Morris,Prattstringmatching26412.2Boundedstaticvariation26612.3Conversionintocontinuationpassingstyle27012.4Etaconversion27312.5Improvementsderivedfrom‘freetheorems’27412.6Exercises27413ApplicationsofPartialEvaluation27713.1Typesofproblemssusceptibletopartialevaluation27713.2Whencanpartialevaluationbeofbene(cid:12)t?28513.3Exercises293VAdvancedTopics29514TerminationofPartialEvaluation29714.1Terminationofonlinepartialevaluators29714.2Terminationofo(cid:15)inepartialevaluators29814.3Binding-timeanalysisensuringtermination30114.4SafetyofBTAalgorithm30514.5Exercises30715ProgramAnalysis30915.1Abstractinterpretation30915.2Closureanalysis31415.3Higher-orderbinding-timeanalysis31915.4Projectionsandpartiallystaticdata32315.5Projection-basedbinding-timeanalysis32815.6Describingthedynamicdata33215.7Summary33315.8Exercises33316LargerPerspectives33516.1Relationstorecursivefunctiontheory33516.2Typesforinterpreters,compilers,andpartialevaluators33716.3Someresearchproblems34617ProgramTransformation34717.1Alanguagewithpatternmatching34717.2Fold/unfoldtransformations35017.3Partialevaluationbyfold/unfold355
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Context: BTAandthetamingofself-application155OnPE(cons(carnames)(carvalues)):On-Env!On-ValueOnPE(cons(carnames)(carvalues))(cid:26)=reduce-cons(reduce-car(lookupnames(cid:26)))(reduce-car(lookupvalues(cid:26)))reduce-car:On-Value!On-Valuereduce-carpv=casepvofinVal(v):inVal(carv)inExp(e):inExp(build-car(e))reduce-cons:On-Value!On-Value!On-Valuereduce-conspv1pv2=casepv1,pv2ofinVal(v1),inVal(v2):inVal(consv1v2)inVal(v1),inExp(re2):inExp(build-cons(build-cstv1)re2)inExp(re1),inVal(v2):inExp(build-consre1(build-cstv2))inExp(re1),inExp(re2):inExp(build-consre1re2)Figure7.3:Afragmentofanoverlygeneralcompiler.programitself!Theresultingprogramwouldtakeasourceprogramandproducetheresultofrunningthesourceprogramonthegiveninputdata:[[crazy]]Lsource=[[int]]L[source,data]=[[source]]Sdata=outputThiscompilerfeatureisaveryuselessone.Conclusion:thecompilermixintismuchmoregeneralthannecessary.Thegeneralityofthecompilermixintisduetothelackofdistinctionbetweeninterpreteractionstraditionallydoneatcompiletime(syntaxanalysis,environmentmanipulation,etc.)andthosetraditionallydoneatruntime(evaluatingsourceprogramexpressions,etc.).Eachtimemixmeetsaconsoperator,forexample,itwilldecideonlinewhetheritisdoingacompiletimeactionoraruntimeaction,andthecompilergeneratedfromtheonlinemixinheritsthisbehaviour.Togetthee(cid:14)ciencyoftraditionalcompilation,theexpression(carnames)intheinterpreterfragmentshouldbeevaluateddirectlybythecompilersincethisisaninstanceofenvironmentmanipulation.Ontheotherhand,residualcodefor(carvalues)shouldbegenerateddirectlybythecompiler.Inneithercaseshoulditbothertocheckwhetherornotthevaluesareconstant.
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Context: Chapter18GuidetotheLiteratureThischaptergivesanoverviewofthecurrentliteratureonpartialevaluation.We(cid:12)rstsketchthehistoryfrom1952to1984.Thenwegiveanoverviewoftheliter-aturegroupedbysubjectlanguage(imperative,functional,logical),bythetech-niquesusedinpartialevaluation(includingbinding-timeanalysis),andbyappli-cations.Finally,wementionsomerelatedtopics.Thebibliography(cid:12)leisavailableforanonymousftpfromftp.diku.dkas(cid:12)lepub/diku/dists/jones-book/partial-eval.bib.Z.Seepage123ofthisbook.18.1Abriefhistoricaloverview18.1.1TheclassicsKleene’ss-m-ntheorem(1952)essentiallyassertsthefeasibilityofpartialevaluation[149].Kleeneprovedthatforanygivenprogram(Turingmachine)forageneralm+n-argumentfunctionf,andgivenvaluesa1;...;amofthe(cid:12)rstmarguments,thereexistsaprogram(aTuringmachine)forthespecializedfunctiong=fa1;...;amwhichsatis(cid:12)esg(b1;...;bn)=f(a1;...;am;b1;...;bn)forallb1;...;bn.Moreover,thereisaprogram(aTuringmachine)whiche(cid:11)ectivelyconstructsthespecializedprogramfromthegeneraloneandtheinputs.ThusKleene’sconstructiveproofprovidesthedesignforapartialevaluator.However,hisdesigndidnot,andwasnotintendedto,provideanyimprovementofthespecializedprogram.Suchimprovement,bysymbolicreductionsorsimilar,hasbeenthegoalinallsubsequentworkinpartialevaluation.Lombardi(ItalyandtheUSA,1964)isprobablythe(cid:12)rstusetotheterm‘par-tialevaluation’,whendiscussingtheuseofLispforincrementalcomputation,orcomputationwithincompleteinformation[175,176].Landin(UK)alsomentionspartialevaluation,butdoesnotde(cid:12)neit,inadiscussiononevaluationoflambdacalculusexpressions[161,p.318].366
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Context: 310ProgramAnalysis+evenoddevenevenoddoddoddeven(cid:2)evenoddevenevenevenoddevenoddApplyingtheideaofabstractevaluationtoprograms,wearriveatabstractinter-pretation:programsmaybeinterpretedconcretelyorabstractly.Abstractinter-pretationofimperativeprogramswasintroducedbySintzo(cid:11)[251]andstudiedindepthbyCousotandCousot[65,64]andNielson[198,200].Hereweoutlinetheabstractinterpretationoffunctionalprograms,whichwaspioneeredbyMycroft[197].15.1.1Variables,conditionals,andfunctionsAnexpressionmaycontainvariables:x*3+7.Ordinaryevaluationofthisexpressionreliesonanenvironmenttosupplya(concrete)valueforx,say8,andevaluatestheexpressionusingthisvalue.Abstractevaluationofthisexpressionnaturallyreliesonanabstractenvironmenttosupplyanabstractvalueforx,sayeven.Continuingtheparityexample,weletEvenOdddenotethesetofabstractpar-ities.Sofarwehaveeven;odd2EvenOdd,butweshallseethatEvenOddmusthavetwomoreelements:onebecauseofconditionals(thissection),andonebecauseofrecursivede(cid:12)nitions(nextsection).Consideraconditionalexpression,suchasifethen3else2.Clearlytheabstractvalueofthe(cid:12)rstbranchisoddandthatofthesecondbranchiseven,butwhatistheabstractvalueoftheentireexpression?Ifwecoulddecidewhethertheconditioneistrueorfalse,therewouldbenoproblem,butingeneralwecannot.Thusthebestdescriptionwecangiveis‘itiseitherevenorodd’:weknownothing,butsofarwelackawayofsayingthis.Weareforcedtointroduceanewabstractvalue>2EvenOdd(pronounced‘top’)torepresenttheabsenceofknowledge.Wealsoprovidefeven;odd;>g(cid:18)EvenOddwithanorderingv,suchthateven<>andodd<>.Whensvt,thatis,whensislessthanorequaltot,thenclearlysismoreprecise(ormoreinformative)thant.Therelationbe-tweenthethreeabstractvaluescanbeshownasfollows,withthesmaller(ormoreinformative)valuesbelowthelarger(orlessinformative)one:>evenodd(cid:8)(cid:8)(cid:8)HHH
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Context: 326ProgramAnalysisAvaluevofthesumdomaineitheris?orhasoneoftheformsv=Int(x)andv=Bl(y).Thenewvalue?islessthanallothers,twovaluesofthesameformarecomparedbythesecondcomponent,andvaluesofdi(cid:11)erentformsareincomparable.Thereare(cid:12)veparticularlyusefulprojectionsonthesumdomain:v=?Int(x)Bl(y)(cid:13)a(v)=???(cid:13)b(v)=?Int(?)Bl(?)(cid:13)c(v)=?Int(x)Bl(?)(cid:13)d(v)=?Int(?)Bl(y)(cid:13)e(v)=?Int(x)Bl(y)Projection(cid:13)asaysthatnopartofthevalueisstatic;(cid:13)bsaysthatthetagsarestaticbutnothingelseis;(cid:13)csaysthatthetagsarestatic,andifthetagisInt,thentheargumentisstatictoo;(cid:13)dsaysthetagsarestatic,andifthetagisBl,thentheargumentisstatic;and(cid:13)esaysthattheentirevalueisstatic.Thus(cid:13)aand(cid:13)ereallyareABSandIDonthesumdomain,andasuitablenamefor(cid:13)bwouldbeTAG.Notethatthereexistotherprojectionsontheuniontype,forinstance(cid:13)0bwith(cid:13)0b(Int(x))=Int(?)and(cid:13)0b(Bl(y))=?.However,suchprojectionsareunlikelytobeusefulasbindingtimes,sinceaspecializercannoteasilyexploitastatictagsuchasIntunlessalltagsarestatic.SomeoftheprojectionsonIntX+BlYcanbewrittenassumsofprojectionsinXandY.Whenever(cid:13)1isaprojectiononXand(cid:13)2isaprojectiononY,theirtaggedsumInt(cid:13)1+Bl(cid:13)2isaprojectiononIntX+BlY,de(cid:12)nedby(Int(cid:13)1+Bl(cid:13)2)(?)=?(Int(cid:13)1+Bl(cid:13)2)(Int(x))=Int((cid:13)1x)(Int(cid:13)1+Bl(cid:13)2)(Bl(y))=Bl((cid:13)2y)Projection(cid:13)aabovecannotbewrittenasasumofprojections,buttheotherfourcan:(cid:13)a=ABS(cid:13)b=IntABS+BlABS(cid:13)c=IntID+BlABS(cid:13)d=IntABS+BlID(cid:13)e=IntID+BlIDWede(cid:12)ne:anadmissiblestaticprojectiononadatatypeisABS,orthesumofadmissibleprojectionsontheconstructorargumenttypes.Thesumofprojections(cid:13)1;...;(cid:13)noverconstructorsc1;...;cniswrittenPni=1ci(cid:13)i.15.4.5ProjectionsonrecursivedatatypedomainsConsidertherecursivedatatype
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Context: 250PartialEvaluationfortheCLanguagepointertypefortheapplicationtobenon-underlined.Thecasesforapplicationofunaryandbinaryoperatorsandexternalfunctionsareallsimilar.Eithertheargumentsarestaticasgivenbythetypeassignment,oralltheargumentsaredynamicandtheapplicationissuspended.Thealloccallreturnsastaticpointer,andanassignmentisnon-underlinedifboththesubex-pressionsarenon-dynamic.Therulesforstructsaremoreliberalthanassumedintheprevioussections.Recallthatwedonotwanttosplitstructsbuttospecializetheirstatic(cid:12)elds.Wecouldthereforeadditionallyrequireanystructexpressionwhoserelevant(cid:12)eldisnon-statictobeunderlined.Giventhede(cid:12)nitionofwell-annotatednessofexpressions,well-annotatednessofstatementscanbede(cid:12)ned.Forexample,anifisstaticifthetestexpressionisstatic.Afunctionde(cid:12)nitionisstaticifitdoesnotcontainadynamicstatement.Inadynamicfunction,everyreturnmustbedynamicsincearesidualfunctionissupposedtoreturnavalueatruntime|notatspecializationtime.Thelaststepistoimposeconditionsregardingthetreatmentofrecursivefunctionsandnon-localside-e(cid:11)ects:e.g.tomakeallassignmentstonon-localvariablesdynamic[6,10].Tothisendtheoutputofapointeranalysisisusefultodetectwhetherand|inthea(cid:14)rmativecase|whichnon-localvariablesafunctionmayreferto.11.5.4Two-levelexecutionofCoreCInthissectionwepresentthecoreofaspecializerfortwo-levelCoreC.Itcomesintwoparts:thetwo-levelexecutionofstatements,andthesurroundingkernelforpolyvariantspecializationofprogrampoints.Two-levelexecutionofstatementsLetthevalueofparameters,localvariables,andglobalvariablesberepresentedbythreeseparatearrayspstore,lstore,andgstorerespectively.Theheapisrepresentedbyanarrayheap,whichalloc()administrates.Theprogramrepresentationisassumedtobeencapsulatedviaexternalfunc-tions.Anexampleisstmtkind(),whichreturnsthetagofaparticularstatementinafunction.Thewholeprogramisrepresentedbythevariablepgm.Theresidualcodeisgeneratedbyexternallibraryfunctionssuchasgen(),whichaddsastatementtotheresidualfunctioncurrentlybeing
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Context: omplementofprojection,332completenessproperty,48,336completion,172
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Context: Continuation-basedreduction219builtintorecentversionsofSimilix,but(currently)doesnotallowpropagationofcontextsunderdynamicconditionals.ThereasonisthatSimilixencapsulatesdynamicconditionalsinsp-functionsandhenceindynamicfunctioncalls,andcontextscannotbepropagatedacrossdynamicfunctioncalls.Theremainderofthissectionpresentstheideainmoredetail.ItisbasedonBondorf’spaper,whichgivesprecisede(cid:12)nitions,theorems,andproofs[31].Intheremainderofthissection,keepinmindthatScheme1hascall-by-valuesemantics.10.5.1ContinuationpassingstyleIncontinuationpassingstyle(CPS)everyfunctiontakesanextraargument:acontinuation.Thecontinuationisafunctionwhichconsumestheresultofthefunctionandproducesthe(cid:12)nalresultofthecomputation.Thusthecontinuationrepresentstheremainderofthecurrentcomputation,orthecontextofthecurrentsubexpressionreduction.Forexample,theordinaryfactorialfunction(define(facn)(if(zero?n)1(*n(fac(-n1)))))hasthefollowingforminCPS:(define(faccpsnK)(if(zero?n)(K1)(faccps(-n1)(lambda(r)(K(*nr))))))TheKargumentisthecontinuation.Itisappliedtotheresultofthefunctioncall,asseeninthe(cid:12)rstbranch(K1).Inthesecondbranch,thecontinuationoftherecursivecallis(lambda(r)(K(*nr))).Thiscontinuationwilltaketheresultroftherecursivecall,multiplyitbyn,andpasstheresulttotheoriginalcontinuationK.NotethattheprograminCPShasthesameoperationalbehaviourastheoriginalone.Therelationbetweenfacandfaccpsisthefollowingforeveryfunction(con-tinuation)Kandnon-negativeintegern:(K(facn))=(faccpsnK)Inparticular,(facn)=(faccpsnid),whereiddenotestheidentityfunction(lambda(r)r).Italsofollowsthat(faccpsnK)=(K(faccpsnid)).Continuationswereinventedinthelate1960sandusedfortransformation,proof,andmathematicaldescriptionofprogramminglanguages;seee.g.[223].Continu-ationsareusedalsoinimplementationsoffunctionalprogramminglanguagessuchasSchemeandStandardML.
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Context: Chapter15ProgramAnalysisThepurposeofautomaticprogramanalysisistoobtainsomeinformationabouttheexecutionofaprogramwithoutactuallyexecutingit,andwithoutusingitsinput.Typicalexamplesofsuchinformationare:‘thevalueofvariableywillalwaysbeaneveninteger’and‘thevalueofvariablegwillneverbethelambdaabstraction(cid:21)x‘.e’.Theinformationmustbevalidforallpossibleexecutionsoftheprogram.Thischapterexplainstheprogramanalysismethodologycalledabstractinter-pretation,anddiscussessomeprogramanalysesrelevanttopartialevaluation:thebinding-timeanalysisforScheme0programs(Section5.2)isrevisited,andaclosureanalysisforhigher-orderlanguagesispresented.Weshowhowtocombinetheseanalysestoobtainabinding-timeanalysisforthehigher-orderlanguageScheme1.Finally,wepresentLaunchbury’sprojection-basedbinding-timeanalysisofpar-tiallystaticstructures.15.1AbstractinterpretationConsideranarithmeticexpressionsuchas8+3andsupposewewanttoknowwhetheritsresultisevenorodd.Wemayevaluateitconcretely,obtainingtheconcretenumber11,whichisodd.However,wemayalsoabstractthevalues8and3bytheirparity(thatis,evenandodd),andconsiderinsteadtheproblemeven+odd.Thismaybeevaluatedabstractly,givingtheabstractresultodd.Clearly,onecouldconsiderotherabstractionsinsteadtoobtaindi(cid:11)erentinfor-mationabouttheexpression,suchasitssign.Forthiswewouldabstractlyevaluatepositive+positiveyieldingpositive.Theimportantobservationisthatwehaveabstractversionsofthevalues(suchas8)aswellasoftheoperationsonthem(suchas‘+’).Thefollowingtablesde(cid:12)neabstractadditionandmultiplicationonfeven;oddg:309
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Context: PartialevaluationforCbytwo-levelexecution245id,(cid:12)d,eid2Identi(cid:12)erslab2Labelsconst2Constants(int,char,double,...)uop,bop2Unaryandbinaryoperatorsbase2Basetypes(int,char,double,...)hCCi::=hdecli(cid:3)hfundefi+CoreChdecli::=htypeiidhtypespeciDeclarationshtypei::=basejhtypei(cid:3)jstructidfhdecli+ghtypespeci::=(cid:15)jhtypespeci[const]hfundefi::=htypei(cid:12)d(hdecli(cid:3))fhdecli(cid:3)hstmti+gFunctionshstmti::=lab:exprhexpiStatementsjlab:returnhexpijlab:gotolabjlab:if(hexpi)lablabjlab:callid=(cid:12)d(hexpi(cid:3))hexpi::=cstconstjvaridExpressionsjstructhexpi.idjindexhexpi[hexpi]jindrhexpijaddrhexpijunaryuophexpijbinaryhexpibophexpijecalleid(hexpi(cid:3))jalloc(id)jassignhexpi=hexpiFigure11.1:AbstractsyntaxofCoreC.11.5PartialevaluationforCbytwo-levelexecutionInthissectionweillustratepartialevaluationforCbymeansofatwo-levelex-ecutionofCoreC.Firstwede(cid:12)neakernellanguageCoreCandextendittoatwo-levelCoreClanguagewithexplicitbinding-timeseparation.Thenwestatewell-annotatednessrequirementsand(cid:12)nallywepresentthespecializationkernel.ThesubsetofCweconsiderinthissectionexcludesfunctionpointersandunions.11.5.1TheCoreCkernellanguageAgoodwaytopartiallyevaluatealarge-scalelanguageisby‘syntacticdesugaring’:totranslateitintoasmallerkernellanguagecapturingtheessentialconstructsofthesubjectlanguage.TheabstractsyntaxofCoreCisdisplayedinFigure11.1.Allsyntacticmatterssuchastypenamedeclarations,scoping,nesteddeclarations,etc.areassumedtobesolvedduringtransformationfromCtoCoreC.Obviously,mostAnsiCconformingprogramscanautomaticallybetransformedintoanequivalentCoreCrepresentation,andevensothatthestructureoftheprogramispreserved.
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Context: ltypesystem,de(cid:12)nedby(cid:22)T:T=T[(cid:22)T:T=T],butthisisomittedforsimplicity.213Thisisanexampleofawell-annotatednessrequirementwhichistreatedinthenextsubsection.
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Context: 3is very important, as writing good rules is the key to building a detection system. Thechapter also explains different rules that are part of Snort distribution.Chapter 4 is about input and output plug-ins. Plug-ins are parts of the softwarethat are compiled with Snort and are used to modify input or output of the Snort detec-tion engine. Input plug-ins prepare captured data packets before the actual detectionprocess is applied on these packets. Output plug-ins format output to be used for a par-ticular purpose. For example, an output plug-in can convert the detection data to a Sim-ple Network Management Protocol (SNMP) trap. Another output plug-in is used to logSnort output data into databases. This chapter provides a comprehensive overview ofhow these plug-ins are configured and used.Chapter 5 provides information about using MySQL database with Snort. MySQLplug-in enables Snort to log data into the database to be used in the analysis later on. Inthis chapter you will find information about how to create a database in MySQL, con-figure a database plug-in, and log data to the database.Chapter 6 describes ACID, how to use it to get data from the database you config-ured in Chapter 5, and how to display it using Apache web server. ACID is a veryimportant tool that provides rich data analysis capabilities. You can find frequency ofattacks, classify different attacks, view the source of these attacks and so on. ACID usesPHP (Pretty Home Page) scripting language, graphic display library (GD library) andPHPLOT, which is a tool to draw graphs. A combination of all of these results in webpages that display, analyze and graph data stored in the MySQL database.Chapter 7 is devoted to information about some other useful tools that can be usedwith Snort.The system that you will build after going through this book is displayed in Figure1-1 with different components. As you can see, data is captured and analyzed by Snort. Snort then stores this datain the MySQL database using the database output plug-in. Apache web server takes helpfrom ACID, PHP, GD library and PHPLOT package to display this data in a browserwindow when a user connects to Apache. A user can then make different types of querieson the forms displayed in the web pages to analyze, archive, graph and delete data.In essence, you can build a single computer with Snort, MySQL database,Apache, PHP, ACID, GD library and PHPLOT. A more realistic picture of the systemthat you will be able to build after reading this book is shown in Figure 1-2.In the enterprise, usually people have multiple Snort sensors behind every routeror firewall. In that case you can use a single centralized database to collect data from allof the sensors. You can run Apache web server on this centralized database server asshown in Figure 1-3.
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Context: Decisionmakingasaprephase?1457.1Decisionmakingasaprephase?Apartialevaluatortypicallyhastochooseanactioninthefollowingsituations:1.Foreachoperator(+,if,...),should/cantheoperatorbereducedatpartialevaluationtimeorshouldresidualcodebegenerated?2.Foreachvariable/parameterateachprogrampoint,should/canitbeconsid-eredstaticorshoulditbedynamic?3.Foreachfunctioncall/jumpshoulditbeunfolded/compressedorshoulditberesidualized?Allo(cid:15)inespecializers(toourknowledge)relyonaprephase(seeFigure7.1),includingbinding-timeanalysisandpossiblyotheranalyses,toresolvetheproblemofchoosingtheproperactionsindependentlyoftheconcretestaticvalues.(AnexceptionwhichwewillnotdiscusshereisbyGl(cid:127)uck[98].)Notshowninthe(cid:12)gureisthepostphase,oftenusedbybothonlineando(cid:15)inespecializerstoperformlast-minutereductions,unfoldings,orothertransformations.Chapters4&5presentedtypicalo(cid:15)ineprogramspecializers.Staticanddy-namiccomputationsweredistinguishedbyadivisionorbyprogramannotationsconstructedindependentlyoftheconcretevalues.Transitioncompression(Sec-tion4.4.4)wasappliedeverywhereexceptinthebranchesofadynamicconditionalstatement(dynamicbythedivision,thatis).InSection5.5asimilaro(cid:15)ineunfold-ingstrategywasproposedforScheme0.Section5.5.6suggestedahybridstrategy.Theo(cid:15)inepart:acallwasannotatedas‘de(cid:12)nitelydynamic’(calld)ifitap-pearedinabranchofadynamicconditionalorifithadaduplicable,non-variable,dynamicactualparameter.Theonlinepart:forcallsannotated‘perhapsstatic’(calls)itwastestedduringspecializationwhetherallduplicable,dynamicpa-rameterswouldbyboundtovariablesbyapossibleunfolding.Ifso,theunfoldingwouldbeperformed.Manypartialevaluatorsuseacombinationofonlineando(cid:11)linetechniques.Al-mostallspecializerscalled‘o(cid:15)ine’(includingthoseinthepreviouschapters;Chap-ter8presentsanexception)useanonlinetechniquetoensurethatmultipleap-pearancesofthesamespecializedprogrampointshareresidualcode.Concretely,themanipulationofthesetspendingandmarkedinvolvescomparisonofvalues,
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Context: Congruenceandconsistencyofannotations1718.3.1Well-annotatedexpressionsAsimpleandtraditionalwaytoprecludetypecheckerrorsistodeviseatypesystem.Intypedfunctionallanguages,atypeinferencealgorithmsuchasalgorithmWchecksthataprogramiswell-typedpriortoprogramexecution[184].Ifitis,thennorun-timesummandtagsorchecksareneeded.Typecheckingisquitewellunderstoodandcanbeusedtogetaniceformulationoftheproblemtobesolvedbybinding-timeanalysis[104,202].WesawinSection5.7thattyperulescanbeusedtocheckwell-annotatedness,andwenowapplysimilarreasoningtothelambdacalculus.De(cid:12)nition8.1Thetwo-leveltypestareasfollows,where(cid:11)rangesovertypevari-ables:t::=(cid:11)jSjDjt!tAtypeenvironment(cid:28)isamappingfromprogramvariablestotypes.2De(cid:12)nition8.2Let(cid:28)beatypeenvironmentmappingthefreevariablesofatwo-levelexpressiontetotheirtypes.Thenteiswell-annotatedif(cid:28)‘te:tcanbededucedfromtheinferencerulesinFigure8.4forsometypet.2Forexample,thetwo-levelexpressionpower-annofExample8.1iswell-annota-tedintypeenvironment(cid:28)=[n7!S,x7!D].ThewholeexpressionhastypeD,andthepart(fixp...)hastypeS!D!D.Ourlambdacalculusisbasicallyuntyped,butthewell-annotatednessensuresthatallprogrampartsevaluatedatpartialevaluationtimewillbewell-typed,thusensuringspecializationagainsttypeerrors.Thewell-annotatednesscriterionis,however,completelypermissiveconcerningtherun-timepartofatwo-levelexpression.Thusalambdaexpressionwithoutstaticoperatorsistriviallywell-typed|atpartialevaluationtime.Two-levelexpressionsoftypeSevaluate(completely)to(cid:12)rst-orderconstants,andexpressionsoftypet1!t2evaluatetoafunctionapplicableonlyatpartialevaluationtime.ThevaluebyTofatwo-levelexpressionteoftypeDisaone-levelexpressione.Forpartialevaluationweareonlyinterestedinfullyannotatedprogramsp-annthathavetypeD.Inthatcase,T[[p-ann]](cid:26)s(ifde(cid:12)ned)willbeapieceofcode,namelytheresidualprogram.Inourcontext,theresultabouterrorfreedomofwell-typedprogramscanbeformulatedasfollows.Proofisomittedsincetheresultiswell-known.De(cid:12
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Context: mpilerisgiveninFigure7.4.Weseethatitsimplygeneratescode,asexpectedofacompiler.Itdoesnotcontainanytestsonresidualexpressionsasdidthecorrespondingfragmentofthemix-generatedcompilerinFigure7.3;itisde(cid:12)nitelyshorterandquitealotfaster.Sotheuseofbinding-timeannotationsisindeedbeverybene(cid:12)cial.2Equippingtheinterpreterwithanexplicitdivisionforeachfunctionwouldhavethesamee(cid:11)ect.
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Context: 16Introductionalwaysyieldsspecializedprogramsfaithfultotheinputprogram.Forinstance,atargetprogramwillbefaithfultoitssource,andageneratedcompilerwillalwaysbecorrectwithrespecttotheinterpreterfromwhichitwasderived.Second,whenauserconstructsacontext-freegrammar,heorsheismainlyinterestedinwhatstringsitgenerates.ButuseofYaccforcestheusertothinkfromanewperspective:possibleleft-to-rightambiguity.IfYaccrejectsagrammar,theusermayhavetomodifyitseveraltimes,untilitisfreeofleft-to-rightambiguity.Analogously,apartialevaluatorusermayhavetothinkabouthisorherprogramfromanewperspective:howclearisitsbinding-timeseparation?Ifspecializedprogramsaretooslow,itwillbenecessarytomodifytheprogramandretryuntilithasbetterbinding-timeproperties.PartialevaluationisnopanaceaNotallprogramsbene(cid:12)tfromspecialization.Knowingthevalueofxwillnotsigni(cid:12)cantlyaidcomputingxnasinFigure1.2,sincenoactualoperationonxcanbedonebythepartialevaluator.Further,thee(cid:14)ciencyofmix-generatedtargetprogramsdependcruciallyonhowtheinterpreteriswritten.Forexample,iftheinterpreterusesdynamicnamebinding,thengeneratedtargetprogramswillhaveruntimevariablenamesearches;andifitusesdynamiccodecreationthengeneratedtargetprogramswillcontainruntimesourcelanguagetext.SomerecurringproblemsinpartialevaluationRapidprogresshasoccurred,butthereareoftenproblemswithterminationofthepartialevaluator,andsometimeswithsemanticfaithfulnessofthespecializedprogramtotheinputprogram(termination,backtracking,correctanswers,etc.).Further,itcanbehardtopredicthowmuch(ifany)speedupwillbeachievedbyspecialization,andhardtoseehowtomodifytheprogramtoimprovethespeedup.Anincreasingunderstandingisevolvingofhowtoconstructpartialevaluatorsforvariouslanguages,ofhowtotameterminationproblems,andofthemathematicalfoundationsofpartialevaluation.Ontheotherhand,weneedtobeableto(cid:15)makeiteasiertouseapartialevaluator;(cid:15)understandhowmuchspeedupispossible;(cid:15)predictthespeedupandspaceusagefromtheprogrambeforespecialization(cid:15)dealwithtypedlang
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Context: 178PartialEvaluationfortheLambdaCalculusFormofeC(cid:28)(e)=(cid:21)x.e1f(cid:11)x!(cid:11)e1(cid:20)f(cid:11)eg[C(cid:28)(e1)e1e2f(cid:11)e2!(cid:11)e(cid:20)f(cid:11)e1g[C(cid:28)(e1)[C(cid:28)(e2)fixe1f(cid:11)e!(cid:11)e(cid:20)f(cid:11)e1g[C(cid:28)(e1)ife1thene2elsee3fS(cid:20)b(cid:11)e1,(cid:11)e=(cid:11)e2,(cid:11)e=(cid:11)e3,(cid:11)e1>(cid:11)eg[C(cid:28)(e1)[C(cid:28)(e2)[C(cid:28)(e3)cfS=(cid:11)egope1e2fS(cid:20)b(cid:11)e1,(cid:11)e1=(cid:11)e2,(cid:11)e2=(cid:11)eg[C(cid:28)(e1)[C(cid:28)(e2)A(cid:21)-boundvariablexf(cid:11)x=(cid:11)egFreexwith(cid:28)(x)=tft=(cid:11)egEverytypederivationfora(cid:21)-expressionecorrespondsuniquelytoatypelabellingofthesyntaxtreeofe;thatis,toamappingof((cid:21)-expression)occurrencesineintotypeexpressions.Atypelabellingthatarisesfromatypederivationinthisfashioncan,however,equallywellbeviewedasamappingfromthecanonicaltypevariablesassociatedabovewiththeoccurrencesinetotypeexpressions.Consequentlyevery(implicit)typederivationfora(cid:21)-expressionedeterminesuniquelyasubstitutiononthesetypevariablesbymappingeveryothertypevariabletoitself.Byinductiononthesyntaxof(cid:21)-expressionseitcanbeshownthatsuchasubstitutionisasolutionoftheconstraintsystemC(cid:28)(e)andviceversa|everysolutionofC(cid:28)(e)isasubstitutiondeterminedbyatypederivationfore.Sinceeveryimplicittypederivationofecorrespondstoauniquecompletionofewehavethefollowingproposition.Proposition8.3Forevery(cid:21)-expressioneandbinding-timeassumption(cid:28)forethereisaone-to-onecorrespondencebetweenthecompletionsofeandthesolutionsofC(cid:28)(e).28.7.3InsertingliftexpressionsThede(cid:12)nitionofacompletionallowsanysubexpressioneoftypeStobereplacedbylifteoftypeD.Whengivenanunannotatedexpressionethereisnoobviousaprioriwaytodeterminewheretoinsertliftstoobtaincompletions.Oursolutionistoassociatetwotypevariables(cid:11)e1and(cid:22)(cid:11)e1witheachsubexpressione1ofe.Asabove,(cid:11)e1representsthe‘immediate’typeofe1,and(cid:22)(cid:11)e1representsitstypeafterpossible
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Context: Callunfoldingonthe(cid:13)y119unfoldingbutisslightlymoreconservativethannecessary.5.5.2AvoidingduplicationofcodeandcomputationsAnunfoldingstrategymustalsoavoidduplicationofcodeandcomputation,soweneedtore(cid:12)neourstrategy.Recallthatwhenunfoldingacall(calls(f(...)(...ej...))),thereducedformofdynamicargumentexpressionejissubstitutedforthecorrespondingdynamicvariablexjinthereducedbodyoffunctionf.Thusduplicationhappensifthevariablexjoccursseveraltimesinthereducedbodyoff.Inthiscaseweshallsaythatejisduplicable.ThenumberofoccurrencesofxjinareducedfunctionbodycanbeapproximatedbyanalysingtheannotatedScheme0program.Forexample,xjoccurstwiceinthereducedformof(consdxjxj),andtwiceinthereducedformof(ifd...xjxj),sincethesedynamicexpressionsreducetothemselves.Countingtheoccurrencesofxjinotherexpressionsissimilar,withoneexception:astaticconditionalreducestoexactlyoneofitsbranches,soxjoccursonlyonceinthereducedformof:(ifs...(...xj...)(...xj...))Forastaticconditionalitissafetotakethemaximum(insteadofthesum)ofthenumberofoccurrenceinthebranches.Thuswecanapproximatethenumberoccurrencesofxjbyananalysisdonewhenannotatingtheprogram.Codeduplicationwillsometimesleadtocomputationduplication.Inthisexam-pleaverymoderatecodeduplicationgivesexponentialcomputationduplication:(define(fn)(if(=n0)1(g(f(-n1)))))(define(gm)(+mm))Unfoldingofthecalltogwouldgive:(define(fn)(if(=n0)1(+(f(-n1))(f(-n1)))))Theunfoldedprogramhasruntimeexponentialinnwheretheoriginalhaslinearruntime.Codeduplicationwithoutcomputationduplicationoccurswhenxjoccurs(once)inbothbranchesofadynamicconditional.5.5.3Unfoldingandside-e(cid:11)ectsScheme0doesnotallowside-e(cid:11)ects,suchasincrementingaglobalvariableX,oroutputtingamessagetoaprinter.However,forcompletenessitshouldbenotedthatside-e(cid:11)ectswouldcreatenewproblemsforunfolding,sinceside-e(cid:11)ectsshouldneverbeduplicated,reordered,ordiscarded.
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Context: 112PartialEvaluationforaFirst-OrderFunctionalLanguage(callge1...ea)andassumees1...esmarethestaticparametersofgaccordingtodiv,anded1...edkarethedynamicones.Wemustsplittheargumentlistintotwo,andwemustmakethecallacallsoracalld.Iftherearenodynamicarguments(thatis,ifm=aandk=0),thenthecallisstatic(shouldbeunfoldedduringspecialization)andistransformedto(callsg(es1...esa)())Iftherearedynamicarguments(thatis,m<aandk>0),thenthecallisdynamic(shouldnotbeunfoldedduringspecialization)andistransformedto(calldg(es1...esm)(ed1...edk))Thisannotationofthecallcorrespondstothesecond-mostconservativecallun-folding(ortransitioncompression)strategymentionedonpage104.WeshalldiscussmoreliberalunfoldingtechniquesinSection5.5below.Notethatwithamonovariantdivisiondivitmaywellhappenthatanargumentexpressionejisstaticaccordingtothebinding-timeanalysis,yetitisinthedynamicargumentlistbecausethejthparameteroffisdynamicaccordingtothedivisiondiv.Conditionalsandbasefunctionapplicationsaremarkedasstaticordynamic,dependingonthebindingtimesoftheirarguments.Aconditional(ife1e2e3)isannotatedas(ifse1e2e3)ife1isstatic,andas(ifde1e2e3)ife1isdynamic.Abasefunctionapplication(ope1...ea)isannotatedas(opse1...ea)ifallejarestatic,andas(opde1...ea)ifsomeejisdynamic.Everyexpressionewhichisstaticaccordingtothebinding-timeanalysis,butappearsinadynamiccontext,mustbemarkedas(lifte).Thedynamiccontextsare:dynamicargumentlists,theargumentlistofanopd,asubexpressionofanifd,thebranchesofanifswhichisitselfinadynamiccontext,thebodyofthegoalfunction,andthebodyofafunctionde(cid:12)nitionhavingatleastonedynamicparameter.Asnotedpreviously,polyvariantbindingtimescanbedealtwithbyinventingsuf-(cid:12)cientlymanyversionsofeachfunction.Forinstance,assumethatatwo-argumentfunctionfiscalledatonepointwithstaticargumentsonly(S;S),andatanotherpointwithonestaticandonedynamicargument(S;D).Thenwe(cid:12)rstconstructtwoversionsfSSandfSDoff,thenannotatetheprogramasabove.Thusasimplewaytohandlepolyvariantbindingtimesistointroduceasu(cid:14)cientsetofbi
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Context: AnoverviewofSimilix205ings.Lambdaabstractionsrequiresomechangestothebinding-timeanalysisandspecialization,mainlybecausetheymaybepartiallystatic.Specializationwithrespecttohigher-ordervaluesandpartiallystaticlambdasisexplainedinSec-tion10.2.Handlinglet-expressionsrequiresanewoccurrencecounting!analysistodecidewhethertheletmaybeunfoldedwithoutriskofduplication.Inaddition,therealSimilixhasa(cid:13)exiblewaytode(cid:12)nenewbasefunctions,andallowslimitedsidee(cid:11)ectsinbasefunctions.Allsidee(cid:11)ectsmustbedynamic(thatis,theymusttakeplaceafterspecialization).Moreover,expressionswithsidee(cid:11)ectscannotbediscarded,norduplicated,norreordered.Basefunctionsandsidee(cid:11)ectswillnotbefurtherdiscussedinthischapter.RecentversionsofSimilixusecontinuation-basedreduction,whichimprovesthebindingtimesofmanyprograms.ItisexplainedinSection10.5.Continuation-basedreductionalsoallowsthehandlingofpartiallystaticdatastructureswithoutduplicatingordiscardingexpressions(seeSection10.6).10.1.1ThestructureofSimilixSimilixworksinthreemainphases:preprocessing,specialization,andpostpro-cessing.Thepreprocessingphaseanalyses,transforms,andannotatesthesubjectprogram,basedonadescriptionofitsinputonly.Thespecializationphasespe-cializestheannotatedsubjectprogramwithrespecttothegivenstaticinput.Thepostprocessingphaseunfoldscallstotrivialresidualfunctions.1.PreprocessingThepreprocessingisdoneinfourmainsteps.1.1Insertanidentitylet-binding(let(xx)...)aroundthebodyofeachfunctionandlambda,foreveryvariablex.Thepurposeistoisolatetheproblemofduplicationfromthatofin(cid:12)niteunfolding.1.2Dobinding-timeanalysis,includingdetectionoflambdasthatmayap-pearindynamiccontexts.Suchlambdasmusthavedynamicparametersandbody,andcannotbeappliedatspecializationtime.1.3Createanewnamedfunction,calledansp-function,foreachdynamicifandforeachdynamiclambda.Allsp-functions(andthegoalfunction)arecalledbydynamiccalls(calld);allotherfunctionsarecalledbystaticones(calls).1.4Analysethenumberofoccurrencesofdynamiclet-boundvariab
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Context: Threemini-languages55SyntaxFollowingisagrammarfor(cid:13)owchartprograms:hProgrami::=readhVari,...,hVari;hBasicBlocki+hBasicBlocki::=hLabeli:hAssignmenti(cid:3)hJumpihAssignmenti::=hVari:=hExpri;hJumpi::=gotohLabeli;jifhExprigotohLabelielsehLabeli;jreturnhExpri;hExpri::=hConstantijhOpihExpri...hExprihLabeli::=anyidenti(cid:12)erornumberThesethConstantiofconstantsandthesethOpiofbasefunctionsareleftunspec-i(cid:12)ed,andagaintheinterpreterinFigure3.3implementsonlyintegerconstants.Thevaluesofthevariablesmentionedintheinitialreadstatementaresuppliedbytheinput.Thevaluesofallother(numeric)variablesareinitiallyzero.ExampleAprogramtocomputethegreatestcommondivisorofnaturalnumbersxandy:readx,y;1:ifx=ygoto7else22:ifx<ygoto5else33:x:=x-ygoto15:y:=y-xgoto17:returnxOperationalsemanticsAsfortheprevioustwolanguageswegivethesemanticsfor(cid:13)owchartprogramsop-erationallybyaninterpreterwritteninML.Asbeforeweuseanabstractprogramsyntax.Thestorebehavesmuchliketheenvironmentinthepreviousinterpreter,butwithadi(cid:11)erence:itmustalsobepossibletochangethevalueboundtoavariable,i.e.toupdateanexistingstore.Equivalencewiththemathematicalse-manticsgiveninthenextsectioncanbeprovenbyinductiononthelengthofacomputation.ThekerneloftheinterpreterinFigure3.3isthecommandexecutionfunctionrun,whichreturnstheprogram’s(cid:12)nalanswer,provideditterminates.The(cid:12)rstargumentisthecurrent‘pointofcontrol’,andthesecondargumentisthecommandtobeexecuted.Ifcontrolisnottransferred,thenthecommandisperformed,thestoreisupdated,andtheinstructioncounterisincremented.Inallcases,thefunctionnthisusedto(cid:12)ndthenextcommandtobeexecuted.Asseeninfunctionlookup,theinitialvalueofanon-inputvariableis0.The
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Context: 104PartialEvaluationforaFirst-OrderFunctionalLanguagepartialevaluatorSimilix(Chapter10).Transitioncompression.Inthe(cid:13)owchartlanguage,transitioncompressionisusedtoimproveresidualprogramsbyremovingsuper(cid:13)uousjumps,suchasajumptoatrivialbasicblockconsistingonlyofajump,etc.Transitioncompressionmaybedoneduringspecialization,orafter.Thesealternativesarealsocalledonthe(cid:13)ycompressionandpostphasecompression.Itwasarguedthatonthe(cid:13)ycompressionispreferable.TheScheme0notioncorrespondingtotransitioncompressionisunfoldingofafunctionapplication.Unfoldingreplacesafunctioncall(callf...ej...)byacopyoff’sbody,whereeveryargumentexpressionejissubstitutedforthecorrespondingformalparameterxj.Atrivialbasicblockcorrespondstoafunctionwhichdoesnothingbutcallan-otherfunction;callstosuchfunctionscanbeeliminatedbyunfolding.Unfoldingismorecomplicatedthan((cid:13)owchart)transitioncompression,asitinvolvessubsti-tutionofargumentexpressionsforformalparametersinadditiontoplaininliningofcode.Thissubstitutionintroducestheriskofcomputationduplication,whichisevenworsethancodeduplication:itwastesruntimeinadditiontospace.Liketransitioncompression,unfoldingmaybedoneonthe(cid:13)yorinapostphase.Asforthe(cid:13)owchartlanguage,unfoldingonthe(cid:13)ygreatlyimprovestheresidualprogramsgeneratedbyspecialization,andinparticularcompilersgeneratedbyself-application.Astrategyforunfoldingonthe(cid:13)ymust(1)avoidin(cid:12)niteunfolding,(2)avoidduplicationofcodeandcomputation,and(3)produceasfewresidualcallsaspossible.Severalstrategieshavebeensuggestedthatattempttosatisfytheserequirements.Themostconservativestrategywoulddonounfoldingonthe(cid:13)y.Thenduringspecialization,afunctionapplicationwouldalwaysreducetoaresidualfunctionapplication,nevertoavalue.Butthentheresultofthefunctionapplicationwouldbedynamic,evenwhenallitsargumentswerestatic,whichmeansthatthestaticdatawouldbeexploitedbadly,andverylittlespecializationisachieved.Thesecond-mostconservativestrategyistounfoldonthe(cid:13)ypreciselythose
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Context: 252IndexAnalysis Control for Intrusion Database (AC-ID), 2–4, 21, 24, 90, 105, 157, 173, 177alert details, 191–192archiving Snort data, 198–200configuring, 181–184DB Setup page, 184, 186defined, 177–179downloading, 179–180features, 178first use of, 184–185graphs, generating, 198installing, 179–181main page, 184, 188–189protocol data, listing, 189–190searching, 192–196tables, 184, 187, 201–202using, 184–202web site, 207whois databases, searching, 197Anomaly-based intrusion detection, 5-A none command line option, 69any keyword, 113Apache Web server, 2, 21, 24, 26, 73, 177web site, 207Application layer headers, 15and signatures, 7Arachnids web site, 129Argus Network Security Services Inc., 73ARP packet header, 241–242Attack signatures, 11Attacks, 11-a unsock command line option, 68Automatic update of Snort rules, 120–125simple message, 120–121sophisticated and complex method, 122–125BBarnyard, 7, 23, 154, 156defined, 207installing, 207-b command line option, 55Berkeley Packet Filter (BPF), 155Book website, 27BPF, 155bpf_file directive, 114Bugtraq, 104CCa parameter, XML module, 146-c command line option, 55-C command line option, 114Cert parameter, XML module, 146Checkpoint, 1, 21Checkpoint Firewall-1, 210–211Checksum field, TCP packet header, 241checksum_mode directive, 115chroot directive, 115Cisco PIX, 210Cisco Systems, 1, 16, 21classification directive, 114classification.config, 38–39, 56, 57, 90, 115Classless Inter-Domain Routing (CIDR), 78–79, 84, 129, 156classtype keyword, 89–93clientonly argument, to stream4_reassembly preprocessor, 137Command line options, Snort, 55–56Common Internet File System (CIFS), 70Computer Associates, 25Configuration parameters, and snort.conf, 112configure script, 31, 56command line options, 31–33command line parameters, 35options, 34–35output, 34prefix option, 34typical session with, 34Console, logging data to, 66content keyword, 93–94content-list keyword, 95Content matching, 94
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Context: Separationofthebindingtimes253Value*gstore;/*Globalstore*/Valueheap[HEAP]/*Heap*/intspec_func(intfunc,Value*store)fintpp;/*Programpoint*/Value*store;/*Storeforactualparameters*//*Initialize*/lstore=alloc_store(func);pending_insert(1,pstore,lstore,gstore);/*Specializeallreachableprogrampoints*/while(!pendingempty())f/*Restorecon(cid:12)gurationaccordingtopending*/pending_restore(pstore,lstore,gstore);pp=pending_pp();pending_processed(pp);/*Two-levelexecution*/hFigure11:4igreturnfunctionindex;gFigure11.5:Functionspecialization.11.6SeparationofthebindingtimesInthissectionwebrie(cid:13)yconsiderbinding-timeanalysisofCwithemphasisonbasicprinciplesratherthandetails[10].DuetothesemanticsofC,andtheinvolvedwell-annotatednessconditions,theanalysisisconsiderablymorecomplicatedthanofthelambdacalculus(forexample),butneverthelessthesamebasicprinciplescanbeemployed.Theanalysisconsistsofthreemainsteps:1.Call-graphanalysis:(cid:12)nd(possibly)recursivefunctions.2.Pointeranalysis:appromixatetheusageofpointers.3.Binding-timeanalysis:computethebindingtimesofallvariables,expres-sions,statements,andfunctions.11.6.1Call-graphanalysisTheaimofthecall-graphanalysisistoannotatecallstopossiblyrecursivefunctionsbyrcall.Thisinformationisneededtosuspendnon-localside-e(cid:11)ectsinresidualrecursivefunctions.Theanalysiscanbeimplementedasa(cid:12)xed-pointanalysis.Theanalysisrecordsforeveryfunctionwhichotherfunctionsitcalls,directlyorindirectly.Initiallythedescriptionisempty.Ateachiterationthedescriptionisupdatedaccordingtothecallsuntila(cid:12)xedpointisreached[4].Clearlytheanalysiswillterminatesincethereareonly(cid:12)nitelymanyfunctions.
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Context: 344LargerPerspectives1.Interpreterint2LSiswell-typedifithastype68(cid:11):(cid:11)S!(cid:11).2.Compilercomp2STL-iswell-typedifithastype8(cid:11):(cid:11)S!(cid:11)T.3.Apartialevaluatormixiswell-typedifithastype8(cid:26):8(cid:27):(cid:26)!(cid:27)!(cid:26)!(cid:27),where(cid:26)rangesover(cid:12)rst-ordertypes.Remark.Thede(cid:12)nitionofawell-typedinterpreterassumesthatallobservableS-typesarealsoL-types.ThusitdoesnottakeintoaccountthepossibilityofencodingS-values.16.2.4Self-applicationandtypesDe(cid:12)nitionsinvolvingself-applicationoften(andrightly)causeconcernastotheirwell-typedness.Weshowherethatnaturaltypesformix-generatedcompilersandtargetprograms(andevencogenaswell)canbededucedfromthefewtyperulesofFigure16.1.Letsource:(cid:11)SbeanS-programdenotingavalueoftype(cid:11).FirstFutamuraprojectionWewishto(cid:12)ndthetypeoftarget=[[mix]]intsource.Thefollowinginferenceconcludesthatthetargetprogramhastype(cid:11)=(cid:11)L,i.e.thatitisanLprogramofthesametypeasthesourceprogram.TheinferenceusesonlytherulesofFigure16.1andinstantiationofpolymorphicvariables.mix:(cid:26)!(cid:27)!(cid:26)!(cid:27)[[mix]]:(cid:26)!(cid:27)!(cid:26)!(cid:27)[[mix]]:(cid:11)S!(cid:11)!(cid:11)S!(cid:11)int:(cid:11)S!(cid:11)[[mix]]int:(cid:11)S!(cid:11)source:(cid:11)S[[mix]]intsource:(cid:11)SecondFutamuraprojectionThepreviousinferenceshowedthat[[mix]]inthasthetypeofacompilingfunction,thoughitisnotacompilerprogram.Wenowwishto(cid:12)ndthetypeofcompiler=6Asaconsequence,[[int]]Lhastype8(cid:11):(cid:11)S!(cid:11),thatistosaytypetS!tforeverytypet.
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Context: xContents3.6.34The uricontent Keyword 1113.7The Snort Configuration File 1123.7.1Using Variables in Rules 1123.7.2The config Directives 1143.7.3Preprocessor Configuration 1163.7.4Output Module Configuration 1163.7.5Defining New Action Types 1173.7.6Rules Configuration 1173.7.7Include Files 1173.7.8Sample snort.conf File 1183.8Order of Rules Based upon Action 1193.9Automatically Updating Snort Rules 1203.9.1The Simple Method 1203.9.2The Sophisticated and Complex Method 1223.10Default Snort Rules and Classes 1253.10.1The local.rules File 1273.11Sample Default Rules 1273.11.1Checking su Attempts from a Telnet Session 1273.11.2Checking for Incorrect Login on Telnet Sessions 1283.12Writing Good Rules 1283.13References 129 Chapter4   Plugins, Preprocessors and Output Modules 1314.1Preprocessors 1324.1.1HTTP Decode 1334.1.2Port Scanning 1344.1.3The frag2 Module 1354.1.4The stream4 Module 1364.1.5The spade Module 1374.1.6ARP Spoofing 1384.2Output Modules 1394.2.1The alert_syslog Output Module 1404.2.1The alert_full Output Module 1434.2.1The alert_fast Output Module 1434.2.1The alert_smb Module 1434.2.1The log_tcpdump Output Module 1444.2.1The XML Output Module 1464.2.1Logging to Databases 1504.2.1CSV Output Module 151
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Context: Exercises275(if(ore1e2)e3e4)Asabove,ife1evaluatestotrue,thetestcanbedeterminedstatically.Supposeasabovethattheentiretestwillbeclassi(cid:12)edasdynamic.Suggestatransformationtosolvetheproblem.Againyoumustavoidcodeduplicationandleavethestrictnesspropertiesunchanged.2ThefollowingexercisesrequireSimilix.Hint:usesomeofSimilix’sutilityfunctionstoshowthebindingtimesoftheprogram,e.g.(showpall)showsallbindingtimesinthecurrentprogram.Exercise12.2ConsiderthefollowingSchemeprogram,whereyouwishtospecializefwithrespecttosomeknownlistxsandunknownn.(define(fnxs)(if(equal?(nthnxs)42)’yes’no))(define(nthnxs)(if(null?xs)’error(if(equal?n0)(carxs)(nth(-n1)(cdrxs)))))Thebinding-timeanalysisperformedinSimilixwillclassifytheequal?operationinfasdynamic.Becauseweknow(nthnxs)willevaluatetooneoftheelementsofxs,wecanapplyTheTricktomaketheequal?operationstatic.Dothisbyintroducingacontinuation.2Exercise12.3ConsiderthefollowingSchemeprogramwhereyouwishtospecializefwithrespecttosomeknownxandunknowny.Thenzwillbemarkedasdynamicandnoneofthebranchesintheif-statementcanbereduced.(define(fxy)(g(hxy)))(define(hxy)(cons(*xx)y))(define(gz)(if(equal?(cdrz)0)(+(carz)2)(-(carz)2)))Introduceacontinuationsothebranchesintheif-statementcanbereduced.2Exercise12.4Extendthe(cid:12)nalversionofthepatternmatchertohandlewildcardsinpatterns,e.g.*canmatchanysymbol.Inwhatwaydoesthisa(cid:11)ectthebindingtimes?2
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Context: 6Chapter1  (cid:127)  Introduction to Intrusion Detection and SnortSnort uses rules stored in text files that can be modified by a text editor. Rules aregrouped in categories. Rules belonging to each category are stored in separate files.These files are then included in a main configuration file called snort.conf. Snort readsthese rules at the start-up time and builds internal data structures or chains to applythese rules to captured data. Finding signatures and using them in rules is a tricky job,since the more rules you use, the more processing power is required to process captureddata in real time. It is important to implement as many signatures as you can using asfew rules as possible. Snort comes with a rich set of pre-defined rules to detect intrusionactivity and you are free to add your own rules at will. You can also remove some of thebuilt-in rules to avoid false alarms.1.1.1Some DefinitionsBefore we go into details of intrusion detection and Snort, you need to learn somedefinitions related to security. These definitions will be used in this book repeatedly inthe coming chapters. A basic understanding of these terms is necessary to digest othercomplicated security concepts.1.1.1.1IDSIntrusion Detection System or IDS is software, hardware or combination of bothused to detect intruder activity. Snort is an open source IDS available to the generalpublic. An IDS may have different capabilities depending upon how complex andsophisticated the components are. IDS appliances that are a combination of hardwareand software are available from many companies. As mentioned earlier, an IDS mayuse signatures, anomaly-based techniques or both.1.1.1.2Network IDS or NIDSNIDS are intrusion detection systems that capture data packets traveling on thenetwork media (cables, wireless) and match them to a database of signatures. Depend-ing upon whether a packet is matched with an intruder signature, an alert is generated orthe packet is logged to a file or database. One major use of Snort is as a NIDS.1.1.1.3Host IDS or HIDSHost-based intrusion detection systems or HIDS are installed as agents on a host.These intrusion detection systems can look into system and application log files todetect any intruder activity. Some of these systems are reactive, meaning that theyinform you only when something has happened. Some HIDS are proactive; they cansniff the network traffic coming to a particular host on which the HIDS is installed andalert you in real time.
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Context: Higher-orderbinding-timeanalysis319bypointwiseinclusion,whichmakesitalattice.Toroughlyformalizetheabstractionandconcretizationfunctions,wemustde-(cid:12)nethesetScheme1ValofvaluesthatcanappearinanordinaryScheme1evalua-tion.Thisisthesetof((cid:12)rst-order)Scheme0values,extendedwithclosureobjects:Scheme1Val=Scheme0Val[ClosureClosure=f(closure‘vv)j‘2Label,vv2Scheme1Val(cid:3)gWiththisde(cid:12)nition,theabstractionandconcretizationfunctionsare(cid:13):LabelSet!}(Scheme1Val)(cid:13)(f‘g)=f(closure‘vv)jvv2Scheme1Val(cid:3)g[Scheme0Val(cid:13)(SiLi)=Si(cid:13)(Li)(cid:11):}(Scheme1Val)!LabelSet(cid:11)(fvg)=fgwhenv2Scheme0Val(cid:11)(f(closure‘vv)g)=f‘g(cid:11)(SiCi)=Si(cid:11)(Ci)Itisclearthat(cid:13)and(cid:11)aremonotonic,andnothardtoseethattheyalsosatisfyrequirements(2)and(3)onabstractionandconcretizationfunctions.15.3Higher-orderbinding-timeanalysisUsingtheclosureanalysis,the(cid:12)rst-orderScheme0binding-timeanalysisfromSec-tion5.2canbeextendedtoahigher-orderbinding-timeanalysisforScheme1.Thisgivesamonovariantbinding-timeanalysiswithoutpartiallystaticstructures.InSection10.1.4wediscussedthreeproblemswithbinding-timeanalysisforScheme1.Problem(1)isthehandlingoflambdaapplications(e1e2).TosolvethisweusetheclosureanalysisfromSection15.2andletPe[[e1]],simply,standforthesetoflambdasthate1mayevaluateto.Problem(2)isthedetectionoflambdasindynamiccontexts.Ifalambda(cid:21)x‘.emayappearinadynamiccontext,thencodemustbegeneratedforthelambda,sox‘mustbedynamicanditsbodyeisinadynamiccontext.Todetectlambdasindynamiccontextsweintroduceanewmap(cid:14)andanewanalysisfunctionBd(inadditiontothemaps and(cid:28)andfunctionsBeandBvknownfromtheScheme0binding-timeanalysis).Problem(3)isthebinding-timeanalysisoflet.InthispresentationweusetheconservativerulesuggestedinSection10.1.4.Thisisunavoidableifthespecializerdoesnotusecontinuation-basedreduction(Section10.5).Ifthespecializerusescontinuation-basedreduction,thenwemightuseinsteadthemoreliberalruleinSection10.5.5.
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Context: Bibliography391[31]A.Bondorf,‘Improvingbindingtimeswithoutexplicitcps-conversion’,in1992ACMConferenceinLispandFunctionalProgramming,SanFrancisco,California(LispPointers,vol.V,no.1,1992),pp.1{10,NewYork:ACM,1992.[32]A.BondorfandO.Danvy,‘Automaticautoprojectionofrecursiveequationswithglobalvariablesandabstractdatatypes’,ScienceofComputerProgramming,16:151{195,1991.[33]A.Bondorf,F.Frauendorf,andM.Richter,AnExperimentinAutomaticSelf-ApplicablePartialEvaluationofProlog,TechnicalReport335,LehrstuhlInformatikV,UniversityofDortmund,Germany,1990.[34]A.BondorfandT.Mogensen,‘Logimix:Aself-applicablepartialevaluatorforProlog’.DIKU,UniversityofCopenhagen,Denmark,May1990.[35]A.Bossi,N.Cocco,andS.Dulli,‘Amethodforspecializinglogicprograms’,ACMTransactionsonProgrammingLanguagesandSystems,12(2):253{302,April1990.[36]R.S.BoyerandJ.S.Moore,‘ProvingtheoremsaboutLispfunctions’,JournaloftheACM,22(1):129{144,January1975.[37]M.Bruynooghe,D.deSchreye,andB.Martens,‘Ageneralcriterionforavoidingin(cid:12)niteunfoldingduringpartialdeductionoflogicprograms’,inV.SaraswatandK.Ueda(eds.),LogicProgramming:InternationalSymposium,pp.117{131,Cambridge,MA:MITPress,1991.[38]M.Bugliesi,E.Lamma,andP.Mello,‘Partialevaluationforhierarchiesoflogictheories’,inS.DebrayandM.Hermenegildo(eds.),LogicProgramming:Proceedingsofthe1990NorthAmericanConference,Austin,Texas,October1990,pp.359{376,Cambridge,MA:MITPress,1990.[39]M.BugliesiandF.Rossi,‘PartialevaluationinProlog:Someimprovementsaboutcut’,inE.L.LuskandR.A.Overbeek(eds.),LogicProgramming:ProceedingsoftheNorthAmericanConference1989,Cleveland,Ohio,October1989,pp.645{660,Cambridge,MA:MITPress,1989.[40]M.A.Bulyonkov,‘Polyvariantmixedcomputationforanalyzerprograms’,ActaInformat-ica,21:473{484,1984.[41]M.A.Bulyonkov,‘Atheoreticalapproachtopolyvariantmixedcomputation’,inD.Bj(cid:28)rner,A.P.Ershov,andN.D.Jones(eds.),PartialEvaluationandMixedComputation,pp.51{64,Amsterdam:North-Holland,1988.[42]M.A.BulyonkovandA.P.Ershov,‘Howdoad-hoccompilerconstructsappearinuniversalm
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Context: Binding-timeanalysisbyabstractinterpretation109inallcallstoginexpressione.ThusBv[[e]](cid:28)ggivesthebinding-timecontextofgine.TheBvfunctionisde(cid:12)nedinFigure5.4.Bv[[e]]:BTEnv!FuncName!BTEnvBv[[c]](cid:28)g=(S;...;S)Bv[[xj]](cid:28)g=(S;...;S)Bv[[(ife1e2e3)]](cid:28)g=Bv[[e1]](cid:28)gtBv[[e2]](cid:28)gtBv[[e3]](cid:28)gBv[[(callfe1...ea)]](cid:28)g=tt(Be[[e1]](cid:28),...,Be[[ea]](cid:28))iff=g=tiff6=gwheret=Faj=1Bv[[ej]](cid:28)gBv[[(ope1...ea)]](cid:28)g=Faj=1Bv[[ej]](cid:28)gFigure5.4:TheScheme0binding-timepropagationfunctionBv.Thisde(cid:12)nitionisexplainedasfollows.Aconstantccontainsnocalltofunctiong,sotheleastupperboundoftheargumentbindingtimesis(S;...;S),namelytheidentityfort.(Comparewiththesumofasetofnumbers.Ifthesetisempty,thesumis0,whichistheidentityfor+.)Avariablexjalsocontainsnocallstog.Aconditional(ife1e2e3)containsthosecallstogwhicharecontainedine1,e2,ore3.Afunctioncall(callfe1...ea)possiblycontainscallstoginthesubexpressionsej.Moreover,iffisg,thenthecallitselfistog,andwemustusethe(cid:12)rstanalysisfunctionBeto(cid:12)ndthebindingtimesoftheargumentsexpressionse1...ea.Finally,abasefunctionapplicationcontainsonlythosecallstogwhichareinthesubexpressionsej.5.2.2ThecongruencerequirementWecannowexpressthecongruencerequirementbyequationsinvolvingBv(whichinturnusesBe).Thecongruencerequirementforamonovariantdivisionsays:ifthereissomecalloffunctiongwherethej’thargumentexpressionisdynamic,thenthej’thparameterofgmustbedescribedasdynamic.Inotherwords,thebindingtimeofthej’thparameterofgmustbeequaltoorgreaterthanthatofthej’thargumentexpressionofeveryapplicationofg.FunctionBvwasde(cid:12)nedsuchthatBv[[e]](cid:28)gisequaltoorgreaterthan(atleastasdynamicas)thebindingtimesofallargumentpatternsoffunctiongine.Takingtheleastupperboundofthesevaluesoverallexpressionsintheprogramgivescongruentbindingtimesforg’sarguments.Thusg’sbinding-timeenvironment(divg)shouldsatisfy:(divg)=Fni=1Bv[[ei]](divfi)gwheref1...fnareallfunctionsinprogrampgm,ande1...enarethecorre-
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Context: Computationinonestageormore7Compilation:2stepssourceprogram(cid:19)(cid:18)(cid:16)(cid:17)?compiler??runtimeinput(cid:19)(cid:18)(cid:16)(cid:17)-targetprogram(cid:23)(cid:22)(cid:20)(cid:21)--output(cid:19)(cid:18)(cid:16)(cid:17)Interpretation:1stepsourceprogram(cid:19)(cid:18)(cid:16)(cid:17)?runtimeinput(cid:19)(cid:18)(cid:16)(cid:17)-inter-preter--output(cid:19)(cid:18)(cid:16)(cid:17)Figure1.3:Compilationintwosteps,interpretationinone.1.3ComputationinonestageormoreComputationalproblemscanbesolvedeitherbysinglestagecomputations,orbymultistagesolutionsusingprogramgeneration.Partialevaluationprovidesawaytogoautomaticallyfromthe(cid:12)rsttothesecond.Toclarifytheproblemsandpayo(cid:11)sinvolvedwe(cid:12)rstdescribetwofamiliarmultistageexamples:1.acompiler,whichgeneratesatarget(=object)programinsometargetlanguagefromasourceprograminasourcelanguage;2.aparsergenerator,whichgeneratesaparserfromacontextfreegrammar.Compilersandparsergenerators(cid:12)rsttransformtheirinputintoanexecutableprogramandthenrunthegeneratedprogram,onruntimeinputsforacompileroronacharacterstringtobeparsed.E(cid:14)ciencyisvital:thetargetprogramshouldrunasquicklyaspossible,andtheparsershoulduseaslittletimeperinputcharacteraspossible.Figure1.3comparestwo-stepcompilativeprogramexecutionwithone-stepinterpretiveexecution.Similardiagramsdescribetwo-stepparsergenerationandone-stepgeneralparsing.1.3.1InterpretersAsourceprogramcanberuninonestepusinganinterpreter:anL-programwecallintthatexecutesS-programs.ThishasasinputtheS-programtobeexecuted,togetherwithitsruntimeinputs.Symbolically,output=[[source]]S[in1,...,inn]=[[int]]L[source,in1,...,inn]
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Context: 316ProgramAnalysis(cid:30)fi=thesetoflambdasthatthebodybodyiofficanevaluateto.(cid:30)‘=thesetoflambdasthatthebodyeof(cid:21)x‘.ecanevaluateto.(cid:26)xij=thesetoflambdasthatfunctionvariablexijcangetboundto.(cid:26)x‘=thesetoflambdasthatlambdavariablex‘cangetboundto.(cid:26)x=thesetoflambdasthatletvariablexcangetboundto.Asbefore,wemustsatisfyourselveswithapproximateinformation,soweactuallyjustrequirethattheset(cid:30)ficontainsallthelambdasthateicanevaluateto,andsimilarlyforthe(cid:26).Thusthesafetyrequirementis:closureanalysisofexpressione1musttellusatleastalllambdasthate1canevaluateto(andpossiblysomemore).Notethatbytherestrictionsthegoalfunction,(cid:30)f1=fgand(cid:26)f1=fg.Intheanalysis,thelabel‘abstractsthesetofalltheclosuresthatcanbebuilt(atruntime)fromtheexpression(cid:21)x‘.e.Example15.1Considerthefollowingprogram(thesuperscript‘isalambdalabel):(define(fxn)(((cid:21)k‘1.kx)(if(evenn)((cid:21)y‘2.y*y)((cid:21)z‘3.2*z))))Wehave(cid:26)k=f‘2;‘3gsincetheexpression(if...)canevaluateto(cid:21)y‘2.y*yaswellas(cid:21)z‘3.2*z.Also,(cid:26)v=fgforallothervariablesv,(cid:30)f=fg,and(cid:30)‘=fgforalllabels‘.Nowconsidertheequivalentbutmorecomplicatedprogram:(define(fxn)(g(((cid:21)x1‘1.(cid:21)k‘2.kx1)x)(h(evenn)((cid:21)x3‘3.x3*x3)((cid:21)x4‘4.2*x4))))(define(gk1z)(k1z))(define(hbca)(ifbca))Herewehave(cid:26)k1=(cid:30)‘1=f‘2g,(cid:26)z=(cid:30)h=f‘3;‘4g,(cid:26)c=f‘3g,(cid:26)a=f‘4g,(cid:26)v=fgforallothervariablesv,(cid:30)‘=fgforallotherlabels‘,and(cid:30)f=(cid:30)g=fg.2Tocompute(cid:30)and(cid:26)ingeneral,wede(cid:12)netwoanalysisfunctionsPeandPv,wherePeiscalledtheclosureanalysisfunctionandPviscalledtheclosurepropagationfunction.TheclosureanalysisfunctionPeisde(cid:12)nedinFigure15.1.TheintentionisthatPe[[e]](cid:30)(cid:26)is(asupersetof)thesetoflambdasthatemayevaluateto.Thevariableenvironment(cid:26)isde(cid:12)nedusingtheanalysisfunctionPv,tobeshownshortly.Theequationsde(cid:12)ningPeinFigure15.1arejusti(cid:12)edasfollows.Aconstantcanevaluatetonolambda.Avari
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Context: 200PartialEvaluationforProlog(cid:2)‘G)(cid:2)1;...;(cid:2)n(cid:2)‘G!true;...;trueIfGisstatic.trueoccursntimes(cid:2)‘G)(cid:15)(cid:2)‘G!failIfGisstatic(cid:2)‘basicN(T1;...;Tn)!basicN((cid:2)(T1);...;(cid:2)(Tn))(cid:2)((S1;...;Sn))t(P11;...;P1n)(cid:31)(cid:2)1(cid:2)01=(cid:2)(cid:14)(cid:2)1(cid:2)01‘R1!R01(cid:1)(cid:1)(cid:1)(cid:1)(cid:1)(cid:1)(cid:1)(cid:1)(cid:1)(cid:2)((S1;...;Sn))t(Pk1;...;Pkn)(cid:31)(cid:2)k(cid:2)0k=(cid:2)(cid:14)(cid:2)k(cid:2)0k‘Rk!R0k(cid:2)‘callN(S1;...;Sn;D1;...;Dm)!(P1n+1=(cid:2)01(D1);...;P1n+m=(cid:2)01(Dm);R01);...;(Pkn+1=(cid:2)0k(D1);...;Pkn+m=(cid:2)0k(Dm);R0k)whereSiarethestaticparameters,DiarethedynamicparametersandN(P11;...;P1n;P1n+1;...;P1n+m):(cid:0)R1...N(Pk1;...;Pkn;Pkn+1;...;Pkn+m):(cid:0)RkaretheclausesforNthatunifywithstaticparametersofthecall.(cid:2)‘callN(S1;...;Sn;D1;...;Dm)!failIfnoclausesforNunifywiththecall(cid:2)‘callN(S1;...;Sn;D1;...;Dm)!callN((cid:2)(S1);...;(cid:2)(Sn))((cid:2)(D1);...;(cid:2)(Dm))whereSiarethestaticparametersandDiarethedynamicparameters.Ade(cid:12)nitionoftheresidualpredicateN((cid:2)(S1);...;(cid:2)(Sn))isaddedtotheresidualprogram.Figure9.5:SpecializationofProloggoals(partI).9.3ConclusionLogimixhasbeensuccessfullyappliedtointerpreters,yieldingcompiledprogramswherevirtuallyallinterpretationoverheadisremoved.Self-applicationofLogimixyieldsstand-alonecompilersandcompilergenerators(seethetablebelow).The(cid:12)guresareforexecutionunderSICStusPrologversion0.6onaSPARCstation2.HeresintistheselfinterpreterusedinLogimixtoexecutestaticsubgoals,usedasastand-aloneprogram,andmixisLogimixitself.Thesizeofthegeneratedcompilergeneratorcogenisapproximately30KBofnon-pretty-printedPrologsource.
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Context: -boot-load-size4\-Aos\-input-charsetutf8\-quiet\-boot-info-table\-oos.iso\isorun:os.isobochs-fbochsrc.txt-q%.o:%.c$(CC)$(CFLAGS)$<-o$@%.o:%.s$(AS)$(ASFLAGS)$<-o$@clean:rm-rf*.okernel.elfos.isoThecontentsofyourworkingdirectoryshouldnowlooklikethefollowingfigure:.|--bochsrc.txt|--iso||--boot||--grub||--menu.lst||--stage2_eltorito|--kmain.c|--loader.s|--MakefileYoushouldnowbeabletostarttheOSwiththesimplecommandmakerun,whichwillcompilethekernelandbootitupinBochs(asdefinedintheMakefileabove).FurtherReading•Kernigan&Richie’sbook,TheCProgrammingLanguage,SecondEdition,[8]isgreatforlearningaboutalltheaspectsofC.22
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Context: Chapter11PartialEvaluationfortheCLanguageLarsOleAndersenThischapterdescribeso(cid:15)inepartialevaluationforasubsetofthepragmaticallyorientedCprogramminglanguage.Cisawidelyusedimperativelanguagewithseveralfeaturesnotavailableinthelanguagesstudiedinthepreviouschapters.Therearedatastructuressuchasstructs,multidimensionalarrays,andpointers;arichvarietyofstatementsandexpressions;andfunctions.Partialevaluationforlarge-scaleimperativelanguagesiscurrentlyanactiveresearcharea.HerewepresentprinciplessuitableforautomaticspecializationofasubstantialsubsetofC,butthetechniquescarryovertorelatedlanguagessuchasPascalandFortran.ThechapterendswithadescriptionofthekernelofaCspecializer.11.1IntroductionThegoalofpartialevaluationise(cid:14)ciency:byspecializingaprogramtopartsofitsinput,afasterspecializedversioncanoftenbeconstructed.Theaimofthischapteristodescribepartialevaluationappliedtoarealisticlarge-scaleimperativeprogramminglanguage.Weshallseethatseveralofthebasictechniquesfromthepreviouschapterscanbeemployed,butnewaspectsmustbeconsideredbecauseofthemorecomplicatedsemanticsofC.Theseinclude,inparticular,specializationoffunctionswithside-e(cid:11)ects,andpointersanddynamicmemoryallocation.Weshallconsideronlyo(cid:15)inepartialevaluationwherethespecializationisper-formedintwostages:(cid:12)rsttheprogramisbinding-timeanalysed,andsubsequentlyitisspecialized.Wearguethattheexplicitseparationofthebindingtimesisanimportantsteppingstoneforsuccessfullyprocessingprogramsthatexploitpointersandaddresses.Withoutapriorbinding-timeanalysis,thepartialevaluatormustbeoverlyconservative,reducingthepotentialgainofaspecialization.SomeknowledgeandexperiencewiththeCprogramminglanguageisexpected,e.g.correspondingtoKernighanandRitchie[147].229
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Context: 356ProgramTransformationApartialevaluationalgorithmThealgorithmisshowninFigure17.4.ItbeginswithaninitialgoalruleL0->R0,whereR0wouldtypicallybeafunctionnameappliedtosomeconstantsandsomegroundterms.Attentionpointsareleftmostinnermostcalls,andtherulesaddedtoPendinghaveform...->fP1...Pn,whereintherightsidebasiccon(cid:12)gurations,eachPiiseitheravariableoragroundterm.Inacallft1...tn,ifargumentticontainsnovariables,thenitwillbereduceduntilitsvaluet0iisknown(itcouldbeorcontainacall).Thispavesthewayforde(cid:12)nitiontocreateaspecializedversionoff,incasesometi’sevaluatetogroundterms.Instantiationisdonetoensurecompleteness|thatallpossiblyapplicableruleswillalsobeprocessed.MainprogramOut:=fg;Pending:=fLinitial->Tinitialg;while9anunmarkedruleL0->R02PendingdoMarkit;forallL->R2Programand(cid:18),(cid:18)0suchthat(cid:18)L=(cid:18)0R0doAdd(cid:18)0L0->T[[(cid:18)R]]toOut;De(cid:12)nitionofTT[[x]]=xT[[ct1...tn]]=cT[[t1]]...T[[tn]]T[[ft1...tn]]=letu1=T[[t1]],...,un=T[[tn]]inlet((cid:18)0,L0->R0)=Makenew(fu1...un)inifL0->R02Pendingthen(cid:18)0L0elseif9ruleL->R2Pgmand(cid:18)with(cid:18)L=fu1...unthenT[[(cid:18)R]]elseAddL0->R0toPending;resultis(cid:18)0L0De(cid:12)nitionofMakenewMakenew(ft1...tn)=((cid:18),gxi1...xim->fs1...sn)wheresi=iftiisgroundthentielsexi(cid:18)(fs1...sn)=ft1...tngisanewfunctionname,dependingonlyonfandthegroundsi’sfi1;...img=fijtiisnon(cid:0)groundgFigure17.4:Partialevaluationbyfold/unfold.Explanation.Inpartialevaluationterms,weassumetheinitialcallcon(cid:12)gurationcontainssomeargumentswhicharegroundtermsandsomevariables.Variables
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Context: 258PartialEvaluationfortheCLanguageCompilergenerationbyself-applicationTheCmixspecializerkernelisself-applicable.Compilergenerationisdemon-stratedbyspecializationofspectoaninterpreterintforanassemblylanguage.ThisexampleistakenfromPaganwhobyhandmakesageneratingextensionoftheinterpreter[213].TheinterpretedprogramPrimescomputesthe(cid:12)rstn’thprimes.ProgramrunRuntimeCodesizetimeratiosizeratio[[Int]](Primes;500)61.9123[[IntPrimes]](500)8.97.01181.0[[spec]](Int;Primes)0.6474[[specInt]](Primes)0.51.27600.6[[spec]](spec;Int)2.2474[[cogen]](Int)0.73.120490.2Thecompiledprimesprogram(inC)isapproximately7timesfasterthantheinterpretedversion.Compilergenerationusingcogencomparedtoself-applicationis3timesfaster.Thecompilergeneratorcogenis2000linesofcodepluslibraryroutines.11.9TowardspartialevaluationforfullAnsiCInthischapterwehavedescribedpartialevaluationforasubstantialsubsetofC,includingmostofitssyntacticalconstructs.Thisdoesnotmean,however,thatanarbitraryCprogramcanbespecializedwell.Therearetwomainreasonsforthis.SomeCprogramsaresimplynotsuitedforspecialization.Ifpartialevaluationisappliedtoprogramsnotespeciallywrittenwithspecializationinmind,itisoftennecessarytorewritetheprograminordertoobtainacleanseparationofbindingtimessoasmuchaspossiblecanbedoneatspecializationtime.Varioustrickswhichcanbeemployedbythebinding-timeengineeraredescribedinChapter12,andmostofthesecarryovertoC.Suchtransformationsshouldpreferablybeautomated,butthisisstillanopenresearcharea.Amoreseriousproblemisduetothesemanticsofthelanguage.InC,program-mersareallowedtodoalmostall(im)possiblethingsprovidedtheyknowwhattheyaredoing.Bydevelopingstrongeranalysesitmaybecomepossibletohandlefeaturessuchasvoidpointerswithoutbeingoverlyconservative,butobviouslyprogramsrelyingonaparticularimplementationofintegersorthelikecannotbehandledsafely.Therecurringproblemisthattheratheropen-endedCsemanticsisnotasu(cid:14)ciently(cid:12)rmbasewhenmeaning-preservingtransformationsaretobeperformedautomatically.
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Context: 112Chapter3  (cid:127)  Working with Snort Rules3.7The Snort Configuration FileSnort uses a configuration file at startup time. A sample configuration filesnort.conf is included in the Snort distribution. You can use any name for the con-figuration file, however snort.conf is the conventional name. You use the -c com-mand line switch to specify the name of the configuration file. The following commanduses /opt/snort/snort.conf as the configuration file./opt/snort/snort -c /opt/snort/snort.confYou can also save the configuration file in your home directory as .snortrc, butspecifying it on the command line is the most widely used method. There are otheradvantages to using the configuration file name as a command line argument to Snort.For example, it is possible to invoke multiple Snort instances on different network inter-faces with different configuration. This file contains six basic sections:(cid:127)Variable definitions, where you define different variables. These variables areused in Snort rules as well as for other purposes, like specifying the location ofrule files.(cid:127)Config parameters. These parameters specify different Snort configurationoptions. Some of them can also be used on the command line.(cid:127)Preprocessor configuration. Preprocessors are used to perform certain actionsbefore a packet is operated by the main Snort detection engine.(cid:127)Output module configuration. Output modules control how Snort data will belogged.(cid:127)Defining new action types. If the predefined action types are not sufficient foryour environment, you can define custom action types in the Snortconfiguration file.(cid:127)Rules configuration and include files. Although you can add any rules in themain snort.conf file, the convention is to use separate files for rules. Thesefiles are then included inside the main configuration file using the includekeyword. This keyword will be discussed later in this chapter.Although the out-of-the-box configuration file works, you need to modify it toadapt it to your environment. A sample configuration file is presented later on.3.7.1Using Variables in RulesIn the configuration file, you can use variables. This is a very convenient way of cre-ating rules. For example, you can define a variable HOME_NET in the configuration file.
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Context: 412Indexbyfold/unfold,355de(cid:12)nition,4forAnsiC,258for(cid:13)owchartlanguage,67forlambdacalculus,163forProlog,192forScheme0,101forScheme1,204mixline,153o(cid:15)ine,84online,84rede(cid:12)nition,341terminationof,297partialevaluator,2typeof,337,343partialfunction,24,26partiallystatic,223,323,370array,240function,212,330heap-allocation,243struct,241pathsinagraph,291patternmatching,28,285Paulson,L.,375PE,seepartialevaluationPEL(language),329pending,86pendingloop,252Penello,T.J.,285Perluette,375phasesino(cid:15)inepartialevaluation,145inonlinepartialevaluation,145Pingali,K.,284Pleban,U.,375Plotkin,G.,38,218,270,340pointer,242birth-placeanalysis,244pointeranalysis,254pointwisedivision,94poly,79in(cid:12)nite,83polyvariantbindingtimes,112binding-timeanalysis,122division,96,103mixedcomputation,68specialization,68,209,232,370poorman’sgeneralization,152,298postphase,145unfolding,104compression,104postprocessing,205prephase,145preprocessing,205procedurecloning,286product,26Cartesian,23ofprojections,325type,26,325programanalysis,seeanalysisprogrampointspecialization,3programspecialization,67programspecializer,71projection,323absent,324admissible,324{326,328complement,332Futamura,seeFutamuraprojectionproduct,325sum,326uniform,327projection-basedbinding-timeanalysis,328Prolog,192,369Pugh,W.,368R(Lambdamix),185ray-tracing,278re-openingclosures,286recipeforself-application,157recursionequations,50,52interpreterfor,53recursivedatatype,32,326recursivefunctiontheory,335redex,47,348Redfun,367,368reduction,45applicativeorder,48normalorder,48ofconstraintsystem,180rules,46Refal,367,369regularexpression,268compilationof,196reorderingofside-e(cid:11)ects,119residualcommand,91residualprogram,71rewriterules,347Richter,M.,194,195,370Ritchie,D.M.,229Rogers,A.,284Rogers,H.,335,336Romanenko,S.A.,94,125,224,368,369,371Ruf,E.,148,150,372
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Context: 248PartialEvaluationfortheCLanguageThemeaningofajumpistocontinue(two-level)programexecutionatthetargetlabel.Themeaningofagotoistogeneratearesidualgotoandtospecializethetargetstatementstothecurrentvaluesofthestaticvariables.Themeaningofacallstatementistoexecutethefunction(whichmustbecompletelystatic),andthemeaningofacallistospecializethecall.Toindicatethedesiredprocessingoffunctioncalls,thetwo-levellanguageisfurthermoreextendedwiththeforms:rcallanducall.Intheformercase,thefunctionisspecializedundertheassumptionthatnostaticside-e(cid:11)ectsunderdynamiccontroloccurinthecalledfunction(cf.Section11.3).13Theucallformindicatesthatthefunctionistobeunfolded.11.5.3Well-annotatedtwo-levelCoreCNotalltwo-levelCoreCprogramspecializewell.Theindispensablecongruencerequirement,thatstaticcomputationmustbeindependentofdynamicvalues,isnotcapturedbythepurelysyntacticde(cid:12)nitionofthetwo-levellanguage.Apro-gramful(cid:12)llingthisprincipleissaidtobewell-annotated.Asetofrulesisneededsuchthatifaprogramsatis(cid:12)esthem,thennobindingtimeerrorcanoccurduringspecialization.Bindingtimescanbeseenastypesinthetwo-levellanguage.Forinstance,aconstanthastypestaticS,anexpressiondependingonadynamicvaluehastypedynamicD.Thiscanbeformalizedbythemeansofatwo-levelbinding-timetypesystem.Abinding-timetype(BTT)Tisgiveninductivelybythegrammar:T::=SjDjT(cid:2)(cid:1)(cid:1)(cid:1)(cid:2)Tj(cid:3)TwhereSandDaregroundtypes.Theconstructor(cid:2)describesthecomponentsofavalueofstructtype.Forexample,astructfintx,y;gcouldbeassignedthebindingtimeS(cid:2)Dmeaningthatxisstaticbutydynamic.Finally,thestarconstructor(cid:3)TdenotesastaticpointertoanobjectofbindingtimeT.Example11.16ConsiderthestructNodede(cid:12)nedinSection11.4,andsupposethatkeyandnextarestatic(cid:12)eldsbutdatadynamic.ThusthetypeTNodeisTNode=S(cid:2)D(cid:2)(cid:3)TNodewheretherecursivede(cid:12)nitionisduetothefactthatthestructNodeisrecursivelyde(cid:12)ned.Formallywewouldhavetoadda(cid:12)xed-pointoperator(cid:22)tothetwo-leveltypesystem,de(cid:1
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Context: 180PartialEvaluationfortheLambdaCalculusinthe‘reduced’system(Rule1a);iftheleft-handsideshavedi(cid:11)erentleft-handsidetypeconstructorsthentheright-handsideisequatedwithD(Rule1b)andtheinequalitiesareeventuallyeliminatedbyRules1gand1h.Thetransitiveclosureofthetransformationrulesisde(cid:12)nedby:CV)+C0ifCV)C0andCV0(cid:14)V)+C0ifCV)+C0,C0V0)+C0forsomeC0,whereV0(cid:14)VdenotesthecompositionofV0andV.WesayCisanormalform(ornormalized)constraintsystemifthereisnoC0suchthatCV)C0foranyV.WesayChasanormalformifthereisanormalformC0suchthatCV)+C0forsomesubstitutionV.Thecorrectnessofthetransformationsiscapturedinthefollowingproposition,whichiseasilyprovedbyinductiononthelengthoftransformationsequencesandbycaseanalysisoftheindividualrulesusingelementarypropertiesof(cid:20)b;(cid:20)f.Proposition8.4(Soundnessandcompletenessoftransformations)LetCV)+C0.ThenSol(C)=f(V0(cid:14)V)jV02Sol(C0)g.2Thetransformationscanbeusedtoderiveanalgorithmfornormalizingcon-straintsystemsbasedonthefollowingproposition.Proposition8.5(Normalizationofconstraintsystems)1.ThetransformationsofFigure8.5areweaklynormalizing;thatis,everyChasanormalform.2.IfC0isanormalformconstraintsystemthen(a)ithasnoequationalconstraints;(b)itisacyclic;(c)itsconstraintsareoftheform(cid:12)!(cid:12)’(cid:20)f(cid:11),(cid:13)(cid:20)b(cid:11)or(cid:11)>(cid:11)0where(cid:11),(cid:11)0aretypevariables;(cid:12)isatypevariableorthetypeconstantD;and(cid:13)isatypevariableorthetypeconstantS.(d)foreveryinequalityconstraintoftheform(cid:12)!(cid:12)’(cid:20)f(cid:11)thetypevariable(cid:11)doesnotoccurontheright-handsideofother(cid:20)f-inequalitiesorontheleft-handsideof(cid:20)b-inequalities;(e)foreveryinequalityconstraintoftheformS(cid:20)b(cid:11)thetypevariable(cid:11)doesnotoccurontheright-handsideof(cid:20)f-inequalitiesoroneithersideof(cid:20)b-inequalities.3.IfCcontainsnoconstraintsoftheform(cid:11)(cid:20)b(cid:11)0where(cid:11)isatypevariableandCS)+C0thenC0containsnoconstraintofthatformeither.
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Context: inding-timeanalysisarepartlyintermingledisoutlinedinSection11.6.12Thisissimilartotheuseofclosureanalysisinhigher-orderpartialevaluation(Chapter10).
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Context: Default Snort Rules and Classes125The script uses a configuration file where many options can be configured. Specif-ically you can configure the following in the configuration file oinkmaster.conf:(cid:127)URL of the location from where it downloads the Snort rules. By default thisURL is http://www.snort.org/downloads/signatures/snortrules.tar.gz or http://www.snort.org/downloads/snortrules.tar.gz. This is configured using the urlkeyword in the configuration file.(cid:127)Files to be updated. By default files ending with .rules, .config, .conf,.txt and .map are updated and all other files are ignored. This is done usingthe update_files keyword.(cid:127)Files to be skipped when updating rules. This is done using the skipfilekeyword. You can use as many skipfiles lines as you like. This option is usefulwhen you have customized rules in some files. When you skip these files, yourcustomized rules will not be overwritten during the update process.(cid:127)You can disable certain rules permanently using the disablesid keyword in theconfiguration file. The tool will not update these rules during the update.Please use the README and INSTALL files that come with the tool. You can usethis tool from a cron script to periodically update your rule set.3.10Default Snort Rules and ClassesSnort comes with a rich set of rules. These rules are divided into different files. Eachfile represents one class of rules. In the source code distribution of Snort, these files arepresent under the rules directory in the source code tree. The following is a list of therule files in Snort 1.9.0 distribution:attack-responses.rulesbackdoor.rulesbad-traffic.ruleschat.rulesddos.rulesdeleted.rulesdns.rulesdos.rulesexperimental.rulesexploit.rulesfinger.rulesftp.rulesicmp-info.rulesicmp.rules
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Context: Output Modules145Some typical names of files created by using this line in snort.conf file in Snort 1.9are: snort_tcpdump.log.1039971287snort_tcpdump.log.1039971389If you use the file command to determine the type of the files created by Snort, anoutput like the following will be displayed.[root@conformix]# file /var/log/snort/snort_tcpdump.log.1039971287 /var/log/snort/snort_tcpdump.log.1039971287: tcpdump capture file (little-endian) - version 2.4 (Ethernet, capture length 1514)[root@conformix]#This output shows that this file is in rcpdump format. Now you can display thecontents of this file (the captured data) using the tcpdump command as follows:[root@conformix]# tcpdump -v -r  /var/log/snort/snort_tcpdump.log.1039971287 11:55:03.163301 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 269 (ttl 150, id 0, len 297)11:55:03.166078 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 325 (ttl 150, id 1, len 353)11:55:03.168592 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 253 (ttl 150, id 2, len 281)11:55:03.170912 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 245 (ttl 150, id 3, len 273)11:55:03.173415 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 289 (ttl 150, id 4, len 317)11:55:03.175796 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 265 (ttl 150, id 5, len 293)11:55:03.178429 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 319 (ttl 150, id 6, len 347)11:55:03.181288 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 317 (ttl 150, id 7, len 345)11:55:03.183845 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 321 (ttl 150, id 8, len 349)11:55:03.186581 192.168.1.1.1901 > 239.255.255.250.1900:  [udp sum ok] udp 313 (ttl 150, id 9, len 341)[root@conformix]#This is especially useful if you want to create log files in binary format and thenuse tcpdump to analyze the log files later.
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Context: Index413Runge-Kuttaintegration,287runningtime,41,131Rytz,B.,371S-expression,29s-m-nfunctionproperty,336s-m-ntheorem,72,366safedivision,331safetyofbinding-timeanalysis,106ofclosureanalysis,316ofspeedupanalysis,135ofspeedupinterval,133Sahlin,D.,195,369,370Sandewall,E.,367,368Sands,D.,370Sardu,G.,369scanner,257Scheme,101,204,225,368Scheme0,101,102specializersourcecode,376two-level,110,111Scheme1,206two-level,207Scherlis,W.,373,374Schism,263,368Schmidt,D.A.,371,375Sch(cid:127)on(cid:12)nkel,M.,28Schooler,R.,368scienti(cid:12)ccomputing,287scope,53secondFutamuraprojection,13,86,344selector,28self-application,13,88,91,116,125,194,215,256double,14recipefor,157tamingbyBTA,153self-dependency,306self-interpretationlambdacalculus,164self-interpreter,139,336semanticallyequivalent,340semanticsoftypes,26,34,339semantics-directedcompilergeneration,375Semilux,369Sestoft,P.,101,118,367,368,370,371Shapiro,E.,373Shepherdson,J.C.,195,369side-e(cid:11)ect,119analysis,198nonlocal,235static,238underdynamiccontrol,236Similix,204,264,272,368,371binding-timeanalysis,322simpleloop,134single-valued,24Sintzo(cid:11),M.,310SIS,375sizeanalysis,302Smith,D.A.,372solutionofconstraintset,177S(cid:28)ndergaard,H.,101,367,368,370sorting,291sourceprogram,8,39sp-function,205,217specialization,205kernel,150point,217Prologgoals,198specializedprogrampoint,78,103speedupbound,128falselysuperlinear,130function,128inloop,134interval,132,134safetyof,133linear,128superlinear,347speedupanalysis,132experiments,136safetyof,135stagingtransformations,373state,98statetransition,98statementdynamic,230static,230static,77,102,166,197always,149goal,197part,324perhaps,145projection,324statement,230variable,301Steensgaard,B.,224,370,371store,57,302storetransformer,57stringmatching,seeKnuth{Morris{Prattstructspecialization,241structuraloperationalsemantics,38
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Context: 56Chapter2  (cid:127)  Installing Snort and Getting StartedThere are many other options which are less frequently used. These options willbe discussed in related sections later on. The functionality of some command lineoptions can be achieved through snort.conf file as well.2.5Step-By-Step Procedure to Compile and Install Snort From Source CodeInstalling Snort from the RPM package is very easy since you have to use only onecommand, “rpm -install <snort_file_name.rpm>”. However, as you haveseen, installing from the source code requires much more work. To summarize the pro-cess of installing from the source code, here is a step-by-step procedure:(cid:127)Download source code file from http://www.snort.org.(cid:127)Unpack the tar file using “tar zxvf <filename.tar.gz>” command.(cid:127)Run the configure script. Typical command line is something like“configure --prefix=/opt/snort --with-mysql -with-snmp -with-opnssl”.(cid:127)Run the make command.(cid:127)Run the “make install” command.(cid:127)Create a directory /var/log/snort.(cid:127)Create a directory /opt/snort/etc.(cid:127)Create a directory /opt/snort/rules.(cid:127)Copy snort.conf to /opt/snort/etc directory.(cid:127)Copy classification.config file to /opt/snort/etc directory.(cid:127)Copy reference.config file to /opt/snort/etc directory.(cid:127)Copy all rule files to /opt/snort/rules directory.(cid:127)Create startup script snortd and copy it to /etc/init.d directory. Createits links in /etc/rcx directories, where x is a run level number, so that Snortstarts at the boot time.(cid:127)If you are using MySQL with Snort, it should be started before starting Snort.2.6Location of Snort FilesSnort files can be categorized as follows:(cid:127)The Snort binary files, which is the actual executable.(cid:127)The Snort configuration file, which is typically snort.conf.
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Context: 378TheSelf-ApplicableScheme0SpecializerA.1.3Hints,andrequirementsontheinputThegoalfunctionmustneverbecalled(byanotherfunctionintheprogram)withotherbindingtimepatternsthanthatspeci(cid:12)edatbindingtimeanalysis.Thiscanberelaxedformonovariantanalysis:itmustnotbecalledwithbindingtimeswhicharenotsmallerthanthegivenone.Forpolyvariantanalysis:itmustnotbecalledwithbindingtimeswhicharesmallerthanthegivenone.Notethatallresidualprogramstakeexactlyoneinput:thelistofdynamicargumentvalues.A.2DatastructuresintheScheme0specializerA.2.1RepresentationofScheme0programsThesyntaxcloselyfollowsthatgiveninFigure5.1,soaprogrammusthaveatleastonede(cid:12)nition,andfunctionde(cid:12)nitionsmayhavezeroormorearguments.program::=(def...def)def::=(define(funcnamevar...var)exp)exp::=()|number|var|(quoteS-expression)|(ifexpexpexp)|(callfuncnameexp...exp)|(opbasefunctionexp...exp)VariablesareSchemesymbols,thatis,non-numericatomsdi(cid:11)erentfrom().Func-tionnamesmayalsobesimpleSchemesymbols,butinresidualprograms,afunc-tionnameisapair(annotatedname.staticvalues)ofanannotatedfunctionnameandthevaluesofthefunction’sstaticargumentsforthisvariant.Annotatedfunctionnamesarethosefoundintheannotatedsubjectprogramsgiventothespecializer(seebelow).A.2.2Representationoftwo-levelScheme0programsThesyntaxofannotated(or,two-level)Scheme0programsisveryclosetothatgiveninFigure5.5.
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Context: From(cid:13)owchartstofunctions105abstractsallresidualexpressionsandvalues(dynamicresults).Thisisthemethodweshallusehere.Itwasusedalsointhevery(cid:12)rstself-applicablepartialevaluator[135]andisusedinthepartialevaluatorSimilix(seeChapter10).Theothermethodemploysatypeinferencesystemtobuildasetofconstraintsonthebindingtimesofallvariables.Aconstraintisaninequalityonthebindingtimesofvariables.Forinstance,ifxdependsony,wewanttoexpressthecongruencerequirement‘ifyisdynamic,thenxmustbedynamictoo’.WiththeorderingS<Donthebinding-timevalues,andwritingbt(x)forthebindingtimeofx,therequirementistheconstraintbt(y)(cid:20)bt(x).Theconstraintsetcanbesolvedsubsequently,givingthebindingtimesforeachvariable.Variantsofthismethodareusedinpartialevaluatorsforthelambdacalculus(Chapter8),andforasubsetofC(Chapter11).Annotationsforexpressingdivisions.Aprogramdivisiondivcanbegiventothespecializerintwoways:separatelyasamappingfromfunctionnamestovariableclassi(cid:12)cations,orintegratedintothesubjectprogramasannotation.Divisionsandannotationsprovidethesameinformationinslightlydi(cid:11)erentways.Adivisiondescribesthebindingtimeofavariableorexpression,whereasanannotationtellshowtohandleprogramphrasesatspecializationtime.Annota-tionscanbeconvenientlyrepresentedinatwo-levelsyntax,whichhasastaticandadynamicversionofeachconstructofthelanguage(conditional,functionappli-cation,andbasefunctionapplication).Ingeneral,reductionofthestaticversion(atspecializationtime)willproduceavalue,whereasreductionofthedynamicversionwillnotchangeitsform,onlyreduceitssubexpressions.Annotationsarenotnecessaryinprinciple|therelevantinformationcanalwaysbecomputedfromthedivision|buttheygreatlysimplifythesymbolicreductionofexpressionsinthespecializationalgorithm.ThusweshalluseannotationstorepresentdivisionsintheScheme0specializer.Werequiredivisionstobecongruent.Similarly,annotationsshouldbeconsis-tent.Forinstance,astaticbasefunctionmustbeappliedonlytostaticargumentexpressions;andanargumentexpressioninthestaticargumentlistofa
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Context: 14IntroductionRemark.Thiswayofdoingcompilergenerationrequiresthatmixbewritteninitsowninputlanguage,e.g.thatS=L.ThisrestrictsthepossibilityofmultiplelanguagepartialevaluationasdiscussedinSection1.1.24.1.5.3ThethirdFutamuraprojectionBypreciselyparallelreasoning,cogen=[[mix]][mix,mix]isacompilergenerator:aprogramthattransformsinterpretersintocompilers.Thecompilersitproducesareversionsofmixitself,specializedtovariousinterpreters.Thisprojectionisevenhardertounderstandintuitivelythanthesecond,butalsogivesgoodresultsinpractice.Veri(cid:12)cationofFigure1.4isagainstraightforwardfromthemixequation:[[p]][in1,in2]=[[[[mix]][p,in1]]]in2=...=[[[[[[cogen]]p]]in1]]in2Whileeasilyveri(cid:12)edfromthede(cid:12)nitionsofmixandinterpretation,itisfarfromclearwhatthepragmaticconsequencesoftheseequationsare.Whatisthee(cid:11)ectinpracticeofapplyingprogramstothemselvesasinputs?Isn’tself-applicationmostoftenusedtoshowproblemsimpossibletosolveonthecomputer,asintheproofoftheunsolvabilityofthehaltingproblem?Andevenassumingthattheseequationscanberealizedonthecomputer,howdoesacompilergeneratedmechanicallyasabovecompareine(cid:14)ciencyandstructurewithhandmadecompilers?Answerstothesequestionsformthebulkofthisbook.1.5.4Speedupsfromself-applicationAvarietyofpartialevaluatorssatisfyingalltheaboveequationshavebeencon-structed.Compilation,compilergeneration,andcompilergeneratorgenerationcaneachbedoneintwodi(cid:11)erentways:target=[[mix]][int,source]=[[compiler]]sourcecompiler=[[mix]][mix,int]=[[cogen]]intcogen=[[mix]][mix,mix]=[[cogen]]mixTheexacttimingsvaryaccordingtothedesignofmixandint,andwiththeimplementationlanguageL.Nonetheless,wehaveoftenobservedthatineachcasethesecondwayisabout10timesfasterthanthe(cid:12)rst.Moral:self-applicationcangenerateprogramsthatrunfaster!4Theoutputlanguageofmixmay,however,bedi(cid:11)erentfromitsinputandimplementationlanguages.
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FINAL ANSWER 
Answer: The provided context consists of several PDF files, each focusing on different aspects of programming languages and their analysis. 

**File 1: Partial Evaluation and Automatic Program Generation**

This file explores the concept of partial evaluation, a technique for optimizing programs by specializing them to parts of their input. It covers various aspects of partial evaluation, including:

* **Introduction:** Defines partial evaluation as program specialization and discusses its benefits, including speedup and automatic program generation.
* **Fundamental Concepts in Programming Languages:** Introduces functions, types, recursion, and data types. It also explains the difference between a program (text) and the function it defines.
* **Programming Languages and Interpreters:** Discusses interpreters, compilers, and their running times. It presents three mini-languages: the untyped lambda calculus, first-order recursion equations, and a simple imperative flowchart language.
* **Principles of Partial Evaluation:**  Explores partial evaluators for the flowchart language and the first-order functional language. It delves into techniques for partial evaluation, including binding-time analysis, unfolding, and transition compression.
* **Efficiency, Speedup, and Optimality:**  Defines and measures speedup, analyzes the speedup of flowchart programs, and discusses the optimality of partial evaluators.
* **Online, Offline, and Self-Application:** Compares online and offline partial evaluation, explores the benefits of self-application, and provides a recipe for self-application.
* **Partial Evaluation for Stronger Languages:**  Presents partial evaluators for the untyped lambda calculus, a Prolog subset, and a substantial subset of Scheme.
* **Partial Evaluation for the C Language:**  Discusses techniques for handling data structures, pointers, and dynamic memory allocation in C.
* **Binding-Time Improvements:**  Explores techniques for improving the binding times of programs, including bounded static variation, conversion into continuation passing style, and eta conversion.
* **Applications of Partial Evaluation:**  Discusses types of problems susceptible to partial evaluation and when it can be beneficial.
* **Termination of Partial Evaluation:**  Analyzes the termination of online and offline partial evaluators and discusses binding-time analysis for ensuring termination.
* **Program Analysis:**  Explains abstract interpretation, closure analysis, and higher-order binding-time analysis. It also presents Launchbury's projection-based binding-time analysis for partially static data.
* **Larger Perspectives:**  Relates partial evaluation to recursive function theory, discusses types for interpreters, compilers, and partial evaluators, and outlines some research problems.
* **Program Transformation:**  Explores the relationship between partial evaluation and classical program transformation methods, such as Burstall and Darlington's fold/unfold technique.
* **Guide to the Literature:**  Provides an overview of the literature on partial evaluation, grouped by subject language, techniques, and applications.

**File 2: Intrusion Detection Systems with Snort**

This file focuses on intrusion detection systems (IDS) and specifically on Snort, an open-source IDS. It covers:

* **Introduction to Intrusion Detection and Snort:**  Explains the basics of intrusion detection, including definitions of IDS, NIDS, and HIDS. It also discusses the importance of security zones and levels of trust.
* **Installing Snort and Getting Started:**  Provides a step-by-step procedure for compiling and installing Snort from source code. It also explains the location of Snort files.
* **Working with Snort Rules:**  Discusses the structure of Snort rules, including rule actions, protocols, addresses, port numbers, direction, and rule options. It also explains how to define new action types and how to automatically update Snort rules.
* **Plugins, Preprocessors, and Output Modules:**  Explains the role of plug-ins, preprocessors, and output modules in Snort. It provides information on configuring and using these components, including logging to databases and generating CSV output.
* **Using MySQL Database with Snort:**  Explains how to configure Snort to log data into a MySQL database for later analysis.
* **Analysis Control for Intrusion Detection (ACID):**  Describes ACID, a web-based tool for analyzing Snort alert data. It covers ACID's features, installation, and usage.
* **Other Useful Tools:**  Provides information on other tools that can be used with Snort.

**File 3: LESS: The Little Book About OS Development**

This file focuses on the development of a simple operating system. It covers:

* **Introduction:**  Explains the purpose of the book and provides a brief overview of the OS development process.
* **Page Frame Allocation:**  Discusses managing available memory, including how to access page frames and the use of a kernel heap.
* **User Mode:**  Explains segments for user mode, setting up for user mode, entering user mode, and using C for user mode programs.
* **File Systems:**  Explains the purpose of file systems and presents a simple read-only file system. It also discusses inodes, writable file systems, and virtual file systems.
* **System Calls:**  Discusses designing and implementing system calls.
* **Multitasking:**  Explains how to create new processes, implement cooperative scheduling with yielding, and preemptive scheduling with interrupts. It also discusses programmable interval timers, separate kernel stacks for processes, and difficulties with preemptive scheduling.

This summary provides a high-level overview of the content covered in each file. The files are interconnected, with concepts introduced in earlier files being built upon in later files. 

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