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1、Bunker: A Tamper Resistant Platform for Network TracingStefan SaroiuUniversity of Toronto邢台人才网 xtycwMotivationTodays tracing help build tomorrows systemsISPs view raw network traces as a liabilityTraces can compromise user privacyProtecting users privacy increasingly importantTrace anonymization mit

2、igates these issuesOffline AnonymizationTrace anonymized after raw data is collectedPrivacy risk until raw data is deletedTodays traces require deep packet inspectionHeaders insufficient to understand phishing or P2PPayload traces pose a serious privacy riskRisk to user privacy is too high Two unive

3、rsities rejected offline anonymizationOfflines Privacy VulnerabilitiesTwo types of attacks:Traditional: Network intrusion attacksNew: Raw data can be subpoenaedBoth universities required that subpoenas would not affect privacyOnline AnonymizationTrace anonymized while tracingRaw data resides in RAM

4、onlyDifficult to meet performance demandsExtraction and anonymization must be done at line speedsCode is frequently buggy and difficult to maintainLow-level languages (e.g. C) + “Home-made parsersSmall bugs cause large amounts of data lossIntroduces consistent bias against long-lived flowsSimple Tas

5、ks can be Very SlowRegular expression for phishing: (password)|(form)|(input)|(PIN)|(username)|(script)|(user id)|(sign in)|(log in)|(login)|(signin)|(log on)|(sign on)|(signon)|(passcode)|(logon)|(account)|(activate)|(verify)|(payment)|(personal)|(address)|(card)|(credit)|(error)|(terminated)|(susp

6、end)A-Za-zlibpcre: 5.5 s for 30 M = 44 Mbps maxOnline AnonymizationTrace anonymized while tracingRaw data resides in RAM onlyDifficult to meet performance demandsExtraction and anonymization must be done at line speedsCode is frequently buggy and difficult to maintainLow-level languages (e.g. C) + “

7、Home-made parsersSmall bugs cause large amounts of data lossIntroduces consistent bias against long-lived flowsOur solution: BunkerCombines best of both worldsSame privacy benefits as online anonymizationSame engineering benefits as offline anonymizationPre-load analysis and anonymization codeLock-i

8、t and throw away the key (tamper-resistance)Threat ModelAccidental disclosure:Risk is substantial whenever humans are handling dataSubpoenas:Attacker has physical access to tracing systemSubpoenas force researcher and ISPs to cooperate As long as cooperation is not “unduly burdensomeImplication: Nob

9、ody can have access to raw dataIs Developing Bunker Legal?It Depends on Intent of UseDeveloping Bunker is like developing encryption Must consider purpose and uses of BunkerDeveloping Bunker for user privacy is legalMisuse of Bunker to bypass law is illegalOutlineMotivationDesign of our platformSyst

10、em evaluationCase study: PhishingConclusionsLogical DesigncaptureAnon.KeyOnlineOfflineassembleparseanonymizeOne-Way Interface(anon. data)Capture HardwarecaptureAnon.KeyOnlineOfflineCapture HardwareClosed-box VMassembleparseanonymizeHypervisorencryptdecryptEnc.KeyEncrypted Raw DataOne-WaySocketVM-bas

11、ed ImplementationOpen-box NICOpen-box NICOpen-box VMsave traceloggingmaintenancecaptureAnon.KeyOnlineOfflineCapture HardwareClosed-box VMassembleparseanonymizeHypervisorencryptdecryptEnc.KeyEncrypted Raw DataOne-WaySocketVM-based ImplementationBenefitsStrong privacy propertiesRaw trace and other sen

12、sitive data cannot be leakedTrace processing done offlineCan use your favorite language!Parsing can be done with off-the-shelf componentsKey Technologies“Closed-box VM protects sensitive dataContains all raw trace data & processing codeNo interactive access to closed-box (e.g. no console)Encryption

13、protects on-disk dataRandomly generated key held in volatile memoryData cannot be decrypted upon reboot“Safe-on-reboot VM mitigates hardware attacksOutlineMotivationDesign of our toolSystem evaluationCase study: PhishingConclusionsSoftware Engineering BenefitsOne order of magnitude btw. online and o

14、fflineDevelopment time: Bunker - 2 months, UW/Toronto - yearsWork Deferral Dont do now what you can do laterError RecoverySmall bugs lead to small errors in the trace - not huge gapsOutlineMotivationDesign of our toolSystem evaluationCase study: PhishingConclusionsPhishing is BadCosts U.S. economy h

15、undreds of millionsAffects 1+ million U.S. Internet users2004 - mid 2006: # of phishing sites grew 10 xBanks claim phishing is #1 source of fraudPhishing messages now personalizedHarder to filterTwo Day Hotmail TraceTues Jan 29/08 11:15am - Thurs Jan 31 11:23am,University of Toronto at Mississauga H

16、otmailUsers3,062# of E-mails Received13,438# of From Addresses7,422# of To Addresses25,456Median # of Words in E-mail Body130QuestionsHow often are URLs present in s?How often do people click on links in s?Do people verify an for legitimacy before clicking on a link?Links in ConclusionsTodays tracin

17、g experiments need to look “deep into network activityIP-level trace vs. and browse historySerious privacy concernsPhysical security isnt enough: subpoenasBunker provides the safety of online anonymizationthe simplicity of offline anonymizationAcknowledgmentsAndrew Miklas (U. of Toronto)Alec Wolman

18、(Microsoft Research)Angela Demke Brown (U. of Toronto)Questions?/stefanDesignOpen-box VMXEN Hypervisor(DomainU)Untrusted SoftwareOnline SoftwareClosed-box VM(Domain0)Anon.KeyEnc.KeyCaptureNICEncrypted Raw TraceOpenNICOne-WayInterfaceOffline SoftwarePhishy Mail Leaks through FilterscaptureAnon.KeyOnlineOfflineAnonymized TraceCaptureHardwareassembleparseanonymizeCommodity VMsave traceloggingmaintenancecaptureAnon.KeyOnlineOfflineAnonymized TraceCaptureHardwareInaccessible VMassembleparseanonymizeHypervisorOne-WaySocketCommodity VMsave traceloggingmaintenancecaptureAn