The Human Element: Addressing Human Adversaries in Security Domains by James Pita A ...

223 Pages·2012·3.2 MB·English

TheHumanElement: AddressingHumanAdversariesinSecurityDomains
by
JamesPita
ADissertationPresentedtothe
FACULTYOFTHEUSCGRADUATESCHOOL
UNIVERSITYOFSOUTHERNCALIFORNIA
InPartialFulfillmentofthe
RequirementsfortheDegree
DOCTOROFPHILOSOPHY
(COMPUTERSCIENCE)
December2012
Copyright 2012 JamesPita
Acknowledgments
AnindividualandspecialthankyoubelongstomyadviserMilindTambe. Icannotbegintothank
youenoughforyoursteadfasteffort,determination,anddedicationtoeachofyourstudents. Not
only are you the exemplar of what an adviser should be, but you are genuinely cherished by
anyone who has had the pleasure to work with you. You have made this experience altogether
remarkable due to your outstanding guidance and more importantly your sincere friendship. I
would also like to thank my committee members for helping to guide my research and think
beyond it: Jonathan Gratch, Richard John, Sarit Kraus, Stacy Marsella, and Nicholas Weller. I
wouldparticularlyliketothankSaritKrausforherunparalleledinsights,guidance,andassistance
throughout my career and Richard John for helping me to expand my understanding of experi-
mentalapproaches. Furthermore,Iwouldliketothankmyco-authorsovertheyears: BoAn,Har-
ish Bellamane, Shane Cullen, Manish Jain, Richard John, Christopher Kiekintveld, Sarit Kraus,
Jun-young Kwak, Reuma Magori-Cohen, Rajiv Maheswaran, Janusz Marecki, Thanh Nguyen,
FernandoOrdo´n˜ez,PraveenParuchuri,ChristopherPortway,ShyamsunderRathi,MichaelScott,
EricShieh,ErinSteigerwald,MilindTambe,JasonTsai,CraigWestern,RongYang,andZhengyu
Yin. Yourdedicatedefforts,assistance,guidance,andhardworkmadethisexperienceexception-
allybetter.
ii
Beyond my mentors and collaborators, I would like to thank CREATE, the Los Angeles
WorldAirport(LAWA)police,andtheTransportationSecurityAdministrationforgivingmethe
opportunitytoworkonreal-worldproblemsthathaveadirectimpactonthecommunity. Aspecial
thank you to Erroll Southers for tirelessly promoting ARMOR as a viable approach for critical
securityproblems,ErnestCruzforhiscountlesshoursdevelopingtheoriginalARMORsoftware
package, and Shane Cullen and Erin Steigerwald for their efforts in developing the GUARDS
system. I also want to thank my colleagues at USC and the greater TEAMCORE community.
YouhaveallhelpedmakethesepastyearsaspecialexperienceformeandIwillneverforgetall
thetimeswehavesharedandallthehelpandguidanceyouhavegivenme. Aspecialthanksgoes
toJanuszMareckiforallthelaughsandadviceovertheyears. IwouldalsoliketothankGodfor
thistremendousopportunity. Finally, morethanthankyougoestomymotherDianePita, father
Eugene Pita, and brother Michael Pita. Thank you for a lifetime of support in everything and
anything that I do. Without your unconditional love and support I would not have been able to
get where I am today. Thank you for making me push my limits, explore the world around me,
and expand my horizons. Thank you for all the sacrifices you have made to get me here and for
yourneverendingguidance,effort,support,andlove. Thankyouforbeingthecornerstoneofmy
life.
iii
Table of Contents
Acknowledgments ii
ListofFigures vii
Abstract ix
Chapter1: Introduction 1
1.1 ProblemAddressed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 COBRA/MATCH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.2 SecurityCircumventionGames . . . . . . . . . . . . . . . . . . . . . . 8
1.3 GuidetoThesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Chapter2: Background 11
2.1 StakelbergGames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 BayesianStackelbergGames . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3 StrongStackelbergEquilibrium. . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 DOBSSandBaselineAlgorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4.1 DOBSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.4.2 UNIFORM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.3 MAXIMIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.5 BRQR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.6 SecurityStackelbergGames . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.7 LosAngelesInternationalAirport . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.8 HumanSubjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Chapter3: RelatedWork 32
3.1 ComputingOptimalStackelbergEquilibria . . . . . . . . . . . . . . . . . . . . 32
3.1.1 EfficientSolutionstogeneralBayesianStackelberggames . . . . . . . . 32
3.1.2 EfficientSolutionsforLarge-ScaleSecurityGames . . . . . . . . . . . . 35
3.2 ComputingRobustStrategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 AddressingSuboptimalDecisions . . . . . . . . . . . . . . . . . . . . . . . . . 38
iv
Chapter4: COBRAAlgorithm 42
4.1 KeyIdeas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1.1 BoundedRationality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.1.2 AnchoringTheory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 RobustAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.1 COBRA(0,(cid:15)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.2 COBRA(α,0) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.2.3 COBRA(α,(cid:15)) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.2.4 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.3 EquivalencesBetweenModels . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.4 ExperimentPurpose,Design,andResults . . . . . . . . . . . . . . . . . . . . . 56
4.4.1 PurposeofthisStudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
4.4.2 ExperimentalDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.4.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4.2.2 RewardStructure . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4.2.3 ObservabilityConditions . . . . . . . . . . . . . . . . . . . . 60
4.4.2.4 AlgorithmsandParameters . . . . . . . . . . . . . . . . . . . 62
4.4.2.5 ExperimentalProcedure . . . . . . . . . . . . . . . . . . . . . 66
4.4.3 ExperimentalResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.4.3.1 KeyObservations . . . . . . . . . . . . . . . . . . . . . . . . 70
4.4.3.2 StatisticalSignificance. . . . . . . . . . . . . . . . . . . . . . 71
4.4.3.3 AnalysisofResults . . . . . . . . . . . . . . . . . . . . . . . 74
4.4.4 HandlingObservationalUncertainty . . . . . . . . . . . . . . . . . . . . 78
4.4.5 RuntimeResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Chapter5: MATCHAlgorithm 87
5.1 MATCHAlgorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.2 ExperimentPurpose,Design,andResults . . . . . . . . . . . . . . . . . . . . . 95
5.2.1 PurposeofthisStudy . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.2.2 ExperimentalDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.2.2.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.2.2.2 RewardStructure . . . . . . . . . . . . . . . . . . . . . . . . 98
5.2.2.3 ExperimentalProcedure . . . . . . . . . . . . . . . . . . . . . 100
5.2.3 ResultsforOriginalStructures . . . . . . . . . . . . . . . . . . . . . . . 103
5.2.4 ResultsforNewRewardStructures . . . . . . . . . . . . . . . . . . . . 104
5.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
5.4 λ-Re-estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.5 RuntimeResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
Chapter6: SecurityCircumventionGames 112
6.1 TSASecurityChallenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.1.1 ModelingtheTSAResourceAllocationChallenges . . . . . . . . . . . . 114
6.1.1.1 DefenderStrategies . . . . . . . . . . . . . . . . . . . . . . . 115
6.1.1.2 AttackerActions . . . . . . . . . . . . . . . . . . . . . . . . . 116
6.1.2 CompactRepresentationforEfficiency . . . . . . . . . . . . . . . . . . 117
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6.1.2.1 ThreatModelingforTSA . . . . . . . . . . . . . . . . . . . . 117
6.1.2.2 CompactRepresentation . . . . . . . . . . . . . . . . . . . . . 119
6.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.2.1 SecurityPolicyAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.2.2 RuntimeAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
Chapter7: Conclusions 127
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
7.2 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
Bibliography 133
AppendixA: StatisticalSignificanceTests 139
A.1 StatisticalSignificanceTestsforCOBRA . . . . . . . . . . . . . . . . . . . . . 139
A.2 StatisticalSignificanceTestsforMATCH . . . . . . . . . . . . . . . . . . . . . 140
AppendixB: RewardStructures 141
AppendixC: Strategies 160
AppendixD: ExpectedRewardsforCOBRAExperiments 197
AppendixE: ExpectedResponsePercentagesforCOBRAExperiment 199
AppendixF: StrategiesforvaryingαinCOBRA(α,2.5) 200
AppendixG: ExperimentalInstructions 204
G.1 MaterialforCOBRAExperiments . . . . . . . . . . . . . . . . . . . . . . . . . 204
G.2 MaterialforMATCHExperiments . . . . . . . . . . . . . . . . . . . . . . . . . 207
G.2.1 ObviousGames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
G.3 ExperimentInstructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
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List of Figures
2.1 LAXSecurity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.1 GameInterface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.2 SingleObservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.3 Averageleaderexpectedvalue . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.4 Unobservedcondition-Expectedaveragereward . . . . . . . . . . . . . . . . . 80
4.5 Strategyentropyforvaryingαvalues . . . . . . . . . . . . . . . . . . . . . . . . 81
4.6 Averageexpectedvaluesforvaryingαundertheunlimitedobservationcondition 82
4.7 Comparingruntimes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
5.1 GameInterface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.2 1-NormScatterPlots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
5.3 Originalrewardstructures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.4 ScatterPlotofResults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
5.5 Re-estimatedRewardStructures . . . . . . . . . . . . . . . . . . . . . . . . . . 110
5.6 Runtimeresults . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6.1 PolicyAnalysis: Increasingresourcesfor10areaswith3securityactivitiesperarea123
6.2 X-axis: Areas,Y-axis: Runtime . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
6.3 Runtime: Increasingresourcesfor10areaswith3securityactivitiesperarea . . . 126
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G.1 GameInterface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
G.2 SingleObservation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
viii
Abstract
Recently, game theory has been shown to be useful for reasoning about real-world security set-
tings where security forces must protect critical assets from potential adversaries. In fact, there
have been a number of deployed real-world applications of game theory for security (e.g., AR-
MORatLosAngelesInternationalAirportandIRISfortheFederalAirMarshalsService). Here,
the objective is for the security force to utilize its limited resources to best defend their critical
assets.
An important factor in these real-world security settings is that the adversaries involved are
humans who may not behave according to the standard assumptions of game-theoretic models.
There are two key shortcomings of the approaches currently employed in these recent applica-
tions. First,humanadversariesmaynotmakethepredictedrationaldecision. Insuchsituations,
where the security force has optimized against a perfectly rational opponent, a deviation by the
human adversary can lead to adverse affects on the security force’s predicted outcome. Second,
humanadversariesarenaturallycreativeandsecuritydomainsarehighlydynamic, makingenu-
merationofallpotentialthreatsapracticallyimpossibletaskandsolvingtheresultinggame,with
currentleadingapproaches,wouldbeintractable.
My thesis contributes to a very new area that combines algorithmic and experimental game-
theory. Indeed,itexaminesacriticalprobleminapplyinggame-theoretictechniquestosituations
ix
where perfectly rational solvers must address human adversaries. In doing so it advances the
study and reach of game theory to domains where software agents and humans may interact.
More specifically, to address the first shortcoming, my thesis presents two separate algorithms
to address potential deviations from the predicted rational decision by human adversaries. Ex-
perimental results, from a simulation that is motivated by a real-world security domain at Los
AngelesInternationalairport,demonstratedthatbothofmyapproachesoutperformthecurrently
deployed optimal algorithms which utilize standard game-theoretic assumptions and additional
alternative algorithms against humans. In fact, one of my approaches is currently under evalua-
tioninareal-worldapplicationtoaidinresourceallocationdecisionsfortheUnitedStatesCoast
Guard.
Towards addressing the second shortcoming of enumeration of a large number of potential
adversarythreatcapabilities, Iintroduceanewgame-theoreticmodelforefficiency, whichaddi-
tionallygeneralizesthepreviouslyacceptedmodelforsecuritydomains. Thisnewgame-theoretic
modelforaddressinghumanthreatcapabilitieshasseenreal-worlddeploymentandisundereval-
uationtoaidtheUnitedStatesTransportationSecurityAdministrationintheirresourceallocation
challenges.
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