SecuringIoT-basedCyber-PhysicalHumanSystemsagainstCollaborativeAttacks

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SathishA.PKumar,CoastalCarolinaUniversity,Conway,SC,USABharatBhargavaandGanapathyManiPurdueUniversity,WestLafayette,IN,USARaimundoMacêdoFederalUniversityofBahia,Ondina,Salvador,Bahia,Brazil

IntroductionandBackground

• CPHSisIntegrationofCyber,Physical,andHumanElements.

• InternetofThingsisusedasamethodologytodeployCPHSystems.

• Duetotheirunpredictability,humanbehaviorisdifficulttomodel.

• Dynamichumaninvolvementinthecontextofcollaborativeattacksneedsfurtherresearch– Multipleadversariescollude,interleave,andattack

• ResultsinsophisticatedCPSattacks• Systembehavesinbyzantinemanner

• Securingsuchsystemistougher 2

MotivationandRationale

• CPHSystemsinICU– Riskoflifethreateningsituations

• Stressfulandunfriendlyenvironments– Possibilitiesofattacksarehigh

– Effectiveandimmediateinterventionisneededtoreducetherisk

• Intrusiontolerance,prevention,anddetectionshouldworkincoordinatedandintegratedfashion

• ResearchisneededtostudyhumaninteractionsinvariousrolesinCPHS– Requirespropermodelingandtools

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SecurityFrameworkforIoTBasedCPHSEnvironment

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IoT Based CPHS environmentf(x1(t),x2(t),…xn(t), v1(t), v2(t)…vn(t), h1(t), h2(t),…hn(t),m1(t), m2(t),…mn(t), k(t), u(t))

Threat Modeling in IoT Based CPHS environment

Co-ordinated Intrusion Detection of Malicious Collaborating Entities in CPHS TI(t)

Adaptive Coordinated Intrusion Response

Co-ordinated Intrusion Prevention

Autonomic Intrusion Tolerance Using Byzantine Fault Tolerant Replication

A B

CDE

SecurityFrameworkforIoT BasedCPHSEnvironment(Cont)

• Theproposedframeworkusesafeedbackcontrolscheme.

• Analogoustoahumanbiologicalmodel- whereattackisdetectedbymeasuringthebodyparameters.

• VariousparametersofCPHScomponentsaremonitoredtodetectanattack.

• Ourphilosophyisthatbyidentifyingtheparametersandmonitoringthechangerapidlyinagiventimeframe,theappropriatethreatcanbeidentifiedandacorrectiveactioncanbetaken.5

IoT-basedCPHSenvironment• NotationofIoTbasedCPHSenvironment

– Attacksensitiveparameters(xn(t))• Examples- PacketDrop,QueueLength,EnergyConsumption

– Nonattacksensitiveparameters(vn(t))• Examples– PatientDemographicDetails,VehicleLocation

– Attackparameters(k(t))• Examples- DoS,CommandInjection,ARPSpoofing

– Controlparameter(u(t))• Examples– IDM,Faulttolerance

– Humanbehaviourparameters(h(t))• Examples–LoginPatterns,PasswordChanges,Accessdetails

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ThreatModelinginCPHS- ThreatIndex(TI)

– MetricusedtodetectifaCPHSnodeisunderattackornot.

– TIquantifiesthethreatofnodeinCPHS.

– Computedusingfuzzylogicbasedonsignificantparameters.

TIEvaluationExample

0 163119 208

NS USVS

Number of packets drop, PD

µ(x)1

0

908656 1157

NS US VS

Queue length, QL

µ(x)1

0 1.661.33 1.99

NS

US

VS

Energy Consumption, EC (Joules)

µ(x)1

• NS is normal state, US is uncertain state and VS is vulnerable state• Parameters: x1 is packet drop, x2 is queue length and x3 is energy consumption• μj (xi) is the grade of membership of parameter xi for fuzzy rule j.

• Fortheparametersidentifiedtodetectthreat– Normalstate,UncertainstateandVulnerablestatethresholdsareidentified

• Xaxisindicatesthevaluesoftheparameters• Yaxisindicatesthefuzzymembershipfunctions– Foreg.,ifthepacketdropislessthan119membershipfunctionofNS

is1andtheMFforUSandVSare0– IfthePDisgreaterthan208MFofVSis1andtheMFforUSandNS

are0– IfthePDisexactly163MFofUSis1andtheMFforVSandNSare0

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TIEvaluationExample(Cont.)

TIEvaluationExample(Cont.)• k=numberofstates=3[NS,US,VS]• iisnumberofparameters=3[PD,QL,EC]• misnoofrules=ki =33=27;• Ruleoutput[yj]cantakeanyvaluefrom1to10• Foreachrulej,therulestrength[wj]andruleoutput[yj]areidentified– RulestrengthistheminimumMFvalue[μj (xi)] amongallparametersifor rule j

– Foreg.,forrule7ifμ7 (x1) is 1, μ7 (x2) is 0.5 and μ7 (x3) is 0.25 • Min (μ7 (xi)) is 0.25

– Assuming rule output for rule 7 [[y7] is 7, – then w7y7is 7*0.25 =1.75

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TIEvaluationExample(Cont.)

• Forallmrules– rulestrength[wj]andruleoutput[yj]arecalculated

• TIisthencalculatedas

• ForexampleifonlyonerulehasWj tobe0.25,whoseoutputyj is7andtherestofWjare0

–TIwillbe1.75/0.25=711

∑

∑

=

=

m

jj

m

jjj

w

yw

1

1TI =

DetectingCollaborativeAttacks

• Detectionofmultiplehumanentitiesusingtwokeymechanisms,– DataRoutingInformation(DRI)Table– CrossChecking

• DRItablewillhaveinformationaboutdeviceidentities,networkconnectioninformation,andlogofinteractionsofentities.

• CrosscheckingisnothingbutamechanismwhereinsideentitiescheckeachotherandDRItabletoidentifymaliciousentities. 12

DetectingCollaborativeAttacks

• AnomalydetectionbymeansofdataminingfromuncategorizedsensordataandorderedDRItabledata

• Clustering-layoutapproachtoCPHSystemswhereaCentralMonitor(CM)canvalidatenewentitiesinthesystemandcrosscheckinregulartimeintervals.– CPHsystementitieswillbegroupedinclusters– EachclusterwithCMandbackupCMs– Beaconthecompromisedentities’identitiestootherentitiesinCPHSystems

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DetectingCollaborativeAttacks

• DeceptiveSecurityLoopholes:inthisapproach,CPHSystemwillappeartobevulnerabletolureattackers.

• Eachattempt’sinformationandtypeofattackwillbeclassifiedandstored.– Createaknowledgerepository

• Underlyingsystemanditsvulnerabilities• Defendableattacks• Novelattacks• Attacksources

– Collaborativeattackerscanbeidentifiedwithcrosscheckingtheknowledgerepositories.

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WhyIntrusionToleranceisrequiredinCPHSystems?

• DetectionisNOTalwayspossibleortimelyfeasible.– NovelAttacks– Securityloopholes– Insiders’collaborativeattacks

• Recoveringfromintrusiondetectionistimecritical.– Criticalprocessmaynotrecover– Affectdistributedprocessing– Redundancyfromreplicas– Self-healingiscostly

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CoordinatedIntrusionPreventionUsingCryptographicPrimitives

• DesignHashfunctionbaseddefensemechanism– GenerateCPHSentitybehavioralproofs– Containinformationfromdatatrafficandforwardingpaths

• Measureandevaluateimpactonparameters– Throughputofapplication– Resourcesdepletion– Detectionandmitigationcapability– Extentofsystemunavailability

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Co-ordinatedIntrusionDetectionofMaliciousCollaboratingEntitiesinCPHS

• ThreatIndexTIforIoTnodeiscalculated– Usingattacksensitiveparametersandmachinelearning

• IndicatesvulnerabilityoftheCPHS• TIcanbecomputedoverperiodoftimeandcomparedwithbenchmark

• Datacollectedfromsimulationenvironmentwithandwithoutattacksisusedfortraining

• IfcomputedTI(t)isgreaterthanvulnerablestatethresholdreferenceTI’,thenodeisidentifiedtobeunderthreat 17

Co-ordinatedIntrusionDetectionofMaliciousCollaboratingEntitiesinCPHS- Example

• N1isnodeunderattack• Thresholdsofparameters[PD,QL,EC]areidentifiedtoconstructfuzzyMF

• Basedontheparameters[PD,QL,EC]observedatN1– Fuzzyrulesaregenerated– TIiscalculated– IfvalueofTIis7,itindicatesnodeisunderthreat

• TI<4isnothreat,TI>6isthreat,TIbetween4and6isvulnerable

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AdaptiveCoordinatedIntrusionResponse

• Developandapplyautonomic/self-adaptivetechniquestoimplementadaptivecoordinatedresponseinCPHS

• Ifanodeisunderthreat,neighboringnodesaresubjectedtoresponseandprotectionalgorithm– ToidentifyintruderandisolateintruderfromCPHS

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AdaptiveCoordinatedIntrusionResponseExample

• Fortheparametersobservedforneighboringnodeforanodeunderattack– IftheIftheparameterswithnormalvaluesaregreaterthanabnormalanduncertainvalues

• Thenode isflaggednormalandaccordinglycertainactionplanistaken– Elseiftheparameterswithabnormalvaluesaregreaterthannormalanduncertainvalues

• Thenode isflaggedmaliciousandaccordinglycertainactionplanistaken

– Elseiftheparameterswithuncertainvaluesaregreaterthannormalandabnormalvalues• Thenode isflaggeduncertainandaccordinglycertainactionplanistaken

AutonomicIntrusionToleranceUsingByzantineFault-tolerantReplication

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AutonomicIntrusionToleranceUsingByzantineFault-tolerantReplication(cont.)

• n-t replicastoreplaceuptot compromisedsystems

l Intelligent adversary requires combination of replica diversity, voting and cryptographic schemes

l Dynamic and complex nature of CPHS requires self-manageable behaviour

l Feedback loop for sensing and adapting to current conditions 22

OurOngoingWorkonByzantineReplication

• BFT protocol that implements a series ofperformance optimization mechanisms: requestbatching, replica rejuvenation, etc.

l Needrightconfigurationofthesystemtoachieve:Sizeandtimeoutforbatching,checkpointperiod,rejuvenationperiod,primarybackupfailuredetectiontimeout,etc.

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OurOngoingWorkonByzantineReplication(cont.)

• Developedaself-manageableversionofBFTtooptimizetherelationthroughput/deliverytime.

• Itisonlineadaptivebecausetheobjective“optimizingdelay/throughput”isnotmodifiedatruntime.

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Controller PBFT

BFTparameters

clientactivityprotocol/systemperformance

Self-manageablePBFT

AutonomicBFT:Onestepahead

• BFTAdaptationpoliciesshouldbedynamicallydefinedbyCoordinatedIntrusionResponse.

• DistinctactionplanswilltriggerdistinctadaptationpoliciesoroperationmodesforBFT.Forexample,– ActionPlan3mayrequireBFTtooptimizethroughputtohandleapossibleDoSattack,evenontheexpenseofdelayingservicesresponses.

– OrAction4mayrequireBFTtoimmediatelycheck-pointingstatetodealwithapossibleshutdown.

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ThreatModelingWithHumanEntities

• Nearly95%ofthealltheSecurityincidentsarecausedbyhumanerrors[Report:2014IBM’sCyberSecurityIntelligenceIndex].

• HumanentitiesadduncertaintytoCPHSystems.– Intentional(malicious)errors– Maliciouscollaborativeattacks– Unintentional(commonmistakes)errors– Identitycompromise– Privacybreach

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ThreatModelingWithHumanEntities

• Nearly95%ofthealltheSecurityincidentsarecausedbyhumanerrors[Report:2014IBM’sCyberSecurityIntelligenceIndex].

• HumanentitiesadduncertaintytoCPHSystems.– Intentional(malicious)errors– Maliciouscollaborativeattacks– Unintentional(commonmistakes)errors– Identitycompromise– Privacybreach

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ModelingAttacksUsingCausalRelationships

• Humanerrors(intentionalorintentional)areconsideredasevents(en).– Oneormorecanoccuratthesametime– Theysequentiallyfollowotherevent(s)

• e1à e2à e3e4• Eventscanbe(a)individualattacksor(b)collaborativeattacks

• Thecausalmodel:astateofanindividualattackcausedbyasequenceofintentionalhumanerrorsrepresentsfiniteperiodofindividualattackexecution. 28

Typeofcollaboration

• Weidentifytwodistincteventscalled“positive”and“negative”collaboration.

• Positivehappenswhentwoindependentattackscollaboratetoincreasethenumberandeffectsoftheresultantdamageevents.

• Oneattackinterferingwithanotherattackandnullifyingtheeffectknownasnegativecollaboration.

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ModelingAttacksUsingCausalRelationships(cont.)

• Weemploycausalgraphtomaptheattackpatternsthroughhumanerrors.

• AcausalgraphG=<V,E>forasetofcausalrulesofanattackisalabeleddigraphwith– verticesV={e|events}– edgesE={<p,q>|∃

• acausalrelationshipc• localoperationL• predicateBsuchthat<p,c,q,L,B>isacausalmodel}.

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AdvantagesofCausalModel

• ByidentifyingallattackeventswecanproduceaCausalAttackGraph(CAG):itcanmodelattacksthataresequentialaswellasconcurrent.

• Thepre-conditionsandpost-conditionsofattacksthatsatisfychangedynamically,thecausalmodelcancapturethechangethatthestate-of-artattackgraphreductiontechniquescannot.

• Thecausalmodelcanhelpusinmodellinglargescalenetworks. 31

AdvantagesofCausalModel(cont.)

• Thecausalmodelcandescribetimingofattacks.– Attacksmayneedtobeoperatingwithinaspecifictimeintervalandtraditionalattackgraphanalysisdidnotconsiderit.

• Thecasualmodelcanrepresentunsuccessfulattacks.– Someattemptedattacksareneversuccessfulandcannotbemodeledbytraditionalattackgraphs

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Contributions

• HolisticFrameworktomitigatesecurityissuesinCPHSenvironment

• GuidelinesfordevelopingadaptivedefensemechanismsformaliciouscollaborativeattacksinCPHS.

• Leadstoimprovedunderstandinganddealingwithcollaborativeattacksandcoordinateddefensethrough

– Faultyhumancomponent– Byzantinefaulttolerance,– Identitymanagement(IDM)

• Autonomic,self-adaptivetechniquestoprevent,detectandcounterthoseCPHSattacks.

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Conclusion

• DiscussedsecurityissuesinIoTbasedCPS• HumanparticipationinCPHSdeepensthosesecurityissues

• ProposedholisticsecurityframeworkforIoTbasedCPHS

• ThreatmodelinginvolvinghumanelementsinCPHS

• ProposedresearchquestionsanddirectionsfortheCPHSsecurity

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Questions

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Appendix

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TIEvaluationExample(Contd.)

TI =

FOR PD=174, QL =843 and EC = 1.8Joules

m is no of rules = kn = 33 = 27;

Here, j ε {1, 2, …m }, n is the number of input metrics and k the number of membership functions for each metric

= 11.5/2.5 = 4.6

∑

∑

=

=

m

jj

m

jjj

w

yw

1

1

TI =

Here m is the number of fuzzy rules, j ε {1, 2, …m }, and m = kn where n is the number of input metrics and k the number of fuzzy membership functions.

Here, wj = min(μj (xi)) where μj (xi) indicate MF of significant parameters of that rule.

weight yj à NS, US and VS TI threshold values denoting the particular rule output.

∑

∑

=

=

m

jj

m

jjj

w

yw

1

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TIEvaluationExample(Contd.) FOR PD=174, QL =843 and EC = 1.8Joules

Rule Number (j) μj (PD)μj (QL) μj(EC) Rule Strength, wj , min(μj(PD)μj(QL)

μj(EC))

Output, yj wjyj

10 0.25 0

01 0

20 0.25 0.4

01 0

30 0.25 0.6

01 0

40 0.75 0

01 0

50 0.75 0.4

04 0

60 0.75 0.6

04 0

70 0 0

01 0

80 0 0.4

04 0

90 0 0.6

07 0

100.75 0.25 0

01 0

110.75 0.25 0.4

0.254 1

120.75 0.25 0.6

0.254 1

130.75 0.75 0

04 0

140.75 0.75 0.4

0.44 1.6

150.75 0.75 0.6

0.64 2.4

160.75 0 0

04 0

170.75 0 0.4

04 0

180.75 0 0.6

07 0

190.25 0.25 0

01 0

200.25 0.25 0.4

0.254 1

21 0.25 0.25 0.6 0.25 7 1.7522

0.25 0.75 00

4 023

0.25 0.75 0.40.25

4 124 0.25 0.75 0.6 0.25 7 1.7525

0.25 0 00

7 026

0.25 0 0.40

7 027

0.25 0 0.60

7 0

m is no of rules = kn = 33 = 27;

Here, j ε {1, 2, …m }, n is the number of input metrics and k the number of membership functions for each metric

= 11.5/2.5 = 4.6TI =

∑

∑

=

=

m

jj

m

jjj

w

yw

1

1

39

N1

M0,1

M2,1

M3,1

M4,1

M5,1

Parameter UCLvs UCLus M01to N1 M21toN1 M31to N1 M41to N1 M51toN1 Average

(PD) 208.63 119.1 155/ US 2000/VS 20/NS 20/NS 20/NS 443

(QL) 1157.72 656.0 120/ NS 12000/VS

120/NS 120/NS 120/ NS 2496

(EC) 1.9941 1.34 1.3 /NS 3.92 /VS 2.33 /VS 2.36 /VS 2.61/ VS 2.51

Rule Number (j) μj (PD) μj (QL) μj(EC) Rule Strength, wj , min(μj(PD)μj(QL) μj(EC))

Output, yj wjyj10 0 0 1 020 0 1

01 03

0 0 00

4 040 0 1

04 05

0 1 00

1 060 1 0

04 07

0 1 10

7 081 0 0

01 09

1 0 00

4 0101 0 1

07 0

11 1 1 000 7 012

1 1 111 7 7

TI = = 7/1 = 7∑

∑

=

=

m

jj

m

jjj

w

yw

1

1

Co-ordinatedIntrusionDetectionofMaliciousCollaboratingEntitiesinCPHS- Example