Game Theory for Managing Security in Chemical Industrial Areas

167 Pages·2018·5.05 MB·English

by  Laobing Zhang

Advanced Sciences and Technologies for Security Applications
Laobing Zhang · Genserik Reniers
Game Theory
for Managing
Security in
Chemical
Industrial Areas
Advanced Sciences and Technologies
for Security Applications
SeriesEditor
Anthony J. Masys, Associate Professor, Director of Global Disaster Management,
Humanitarian Assistance and Homeland Security, University of South Florida,
Tampa,USA
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GiselaBichler,CaliforniaStateUniversity,SanBernardino,CA,USA
ThirimachosBourlai,WVU-StatlerCollegeofEngineeringandMineral
Resources,Morgantown,WV,USA
ChrisJohnson,UniversityofGlasgow,UK
PanagiotisKarampelas,HellenicAirForceAcademy,Attica,Greece
ChristianLeuprecht,RoyalMilitaryCollegeofCanada,Kingston,ON,Canada
EdwardC.Morse,UniversityofCalifornia,Berkeley,CA,USA
DavidSkillicorn,Queen’sUniversity,Kingston,ON,Canada
YoshikiYamagata,NationalInstituteforEnvironmentalStudies,Tsukuba,Japan
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(cid:129)
Laobing Zhang Genserik Reniers
Game Theory for Managing
Security in Chemical
Industrial Areas
LaobingZhang GenserikReniers
SafetyandSecurityScienceGroup SafetyandSecurityScienceGroup
DelftUniversityofTechnology DelftUniversityofTechnology
Delft,TheNetherlands Delft,TheNetherlands
ISSN1613-5113 ISSN2363-9466 (electronic)
AdvancedSciencesandTechnologiesforSecurityApplications
ISBN978-3-319-92617-9 ISBN978-3-319-92618-6 (eBook)
https://doi.org/10.1007/978-3-319-92618-6
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Introduction
Weareconvincedthatphysicalsecurityinchemicalindustrialareascanandshould
beimproved,throughouttheworld.Chemicalsubstancesarestoredandprocessedin
largequantitiesinchemicalplantsandchemicalclustersaroundtheglobe,anddueto
thematerials’characteristicssuchastheirflammability,explosiveness,andtoxicity,
theymaycausehugedisastersandevensocietaldisruptionifdeliberatelymisused.
Dealing with security implies dealing with intelligent adversaries and deliberate
actions, as will also be further expounded in the next chapters. Such intelligent
adversariesrequiresmartsolutionsandflexiblemodelsandrecommendationsfrom
thedefender’sside. Suchisonly possible viamathematical modellingandthrough
the use of game theory as a technique for intelligent strategic decision-making
support. In this book, we will elaborate and discuss on how this can be achieved.
Figure1showsanoverviewofthebook.
Fig.1 Organizationof
Ch1: Chemical Security Ch2: Game Theory
thebook
x
Single Plant Protection Multi-Plant Protection
Ch3: Chemical Plant
Protection (CPP) Game
Ch6: Chemical Cluster
Patrolling (CCP) Game
Ch4: Interval CPP Game
Ch5: CPP Game with
Boundedly Rational Attacker
Ch7: Case Study of CPP Game and CCP Game
Ch8: Conclusion and Recommendation
v
vi Introduction
Chapter 1 points out that ‘intentionality’ is the key difference between a (delib-
erate)securityeventanda(coincidental)safetyevent.Theimportanceofprotecting
achemicalplantaswellasprotectingachemicalclusterisillustratedinthechapter.
State-of-the-art literature and governmental regulations are discussed. The lack of
historicaldataandtheexistenceofintelligentadversariesareidentifiedasthemain
challengesforimprovingsecurityinchemicalindustrialareas.
Chapter 2 introduces game theory, which is the main methodology used in this
book. ‘Players’, ‘strategies’, and ‘payoffs’ are the main components of a game
theoreticmodel.The‘commonknowledge’assumptionandthe‘rationality’assump-
tion are the most frequently used assumptions in game theoretic research and are
thoroughlyexplained.Gameswithadiscretesetofstrategiesarealsodiscussed(and
furtherused),sincetheyareeasiertosolveaswellastheybetterreflectrealitythan
gameswithcontinuousstrategies.
Chapters3,4,and5concernthephysicalprotectionofchemicalplantsbelonging
to a single operator. In Chap. 3, a Chemical Plant Protection (CPP) game is
developed, based on the so-called multiple-layer protection approach for chemical
plants.TheCPPgameisabletomodelintelligentinteractionsbetweenthedefender
and the attackers. An analysis of the inputs and outputs of the CPP game is also
provided.
However,theCPPgamesuffersadrawback,thatis,alargeamountofquantita-
tiveinputsisrequired.Chapter4thereforeaddressesthisdisadvantage,byproposing
an Interval CPP game, which is an extension of the CPP game where the exact
numbers of the attacker’s parameters are no longer needed. Instead, in this game,
only the intervals that the parameters will be situated in are required. Thus, the
Interval CPP game considers the defender’s distribution-free uncertainties on the
attackers’parameters, andhencetheinputsfor theIntervalCPP game areeasier to
obtain,forinstance,byusingtheoutputsfromtheAPISRAmethod[1].
AseconddrawbackoftheCPPgameconcernstherationalattackerassumption.
Chapter 5 therefore models bounded-rational attackers into the CPP game. In
Chap.5,threerobustsolutionsareproposedfortheCPPgame,namely,theRobust
solution with epsilon-optimal attackers, the MoSICP solution, and the MiniMax
solution, for addressing attackers who may deviate from strategies having close
payoffs to their ‘best response’ strategy, for addressing attackers who may play
strategieswithhigherpayoffswithhigherprobabilities,andforaddressingattackers
whoonlyaimatminimizingthedefender’smaximalpayoffs,respectively.
Chapter 6 employs game theory for optimizing the scheduling of patrolling in
chemicalclustersorchemicalindustrialparks.AChemicalClusterPatrolling(CCP)
gameisformulated.Boththehazardousnesslevelofeachplantandtheintelligence
ofadversariesareconsideredintheCCPgame,forgeneratingrandombutstrategic
andimplementablepatrollingroutesfortheclusterpatrollingteam.
InChapter7,twoillustrativecasestudiesareelaboratedandinvestigated.Inthe
firstcasestudy,theCPPgameisappliedtoarefinerytoshowhowthegameworks
andwhatresultscanbeobtainedbyimplementingthegame.Therefinerycaseisalso
usedintheAPISRAdocumentforillustrativepurposes.Therefore,theoutputsfrom
Introduction vii
theAPISRAmethodareusedasonepartoftheinputsfortheCPPgame,whileother
inputsoftheCPPgameareillustrativenumbers.Inthesecondcasestudy,theCCP
gameisappliedtoachemicalclustercomposedofseveralplants,eachbelongingto
differentoperators,foroptimizingthepatrollingofsecurityguardsinthemulti-plant
area. Results show that the patrolling route generated by the CCP game well out-
performsthepurelyrandomizedpatrollingstrategyaswellasallthefixedpatrolling
routes.
EightconclusionsaredrawnandninerecommendationsaregiveninChap.8.
Reference
1.API.Securityriskassessmentmethodologyforthepetroleumandpetrochemical
industries.In:780ARP,editor.2013.
Contents
1 ProtectingProcessIndustriesfromIntentionalAttacks:TheState
oftheArt. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 SafetyandSecurityDefinitionsandDifferences. . . . . . . . . . . . . . 2
1.3 SecurityinaSingleChemicalPlant. . . . . . . . . . . . . . . . . . . . . . . 5
1.3.1 TheNeedofImprovingSecurityinChemicalPlants. . . . . 5
1.3.2 ChallengeswithRespecttoImprovingChemicalSecurity. . 8
1.3.3 SecurityRiskAssessmentinChemicalPlants:
State-of-the-ArtResearch. . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.4 DrawbacksofCurrentMethodologies. . . . . . . . . . . . . . . . 17
1.4 ProtectionofChemicalIndustrialParks(CIPs)orSo-Called
ChemicalClusters. .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . 18
1.4.1 SecurityWithinChemicalClusters. . . . . . . . . . . . . . . . . . 18
1.4.2 ChemicalClusterSecurity:State-of-the-ArtResearch. . . . . 19
1.4.3 FuturePromisingResearchDirectionsonCluster
Security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2 IntelligentInteractionModelling:GameTheory. . . . . . . . . . . . . . . . 25
2.1 PreliminariesofGameTheory,SettingtheScene. . . . . . . . . . . . . 25
2.1.1 Introduction.. . . .. . . .. . . .. . . .. . . .. . . . .. . . .. . . .. 25
2.1.2 Players. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.1.3 Strategy(Set). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.1.4 Payoff. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.1.5 TheAssumptionof‘CommonKnowledge’. . . . . . . . . . . . 29
ix
x Contents
2.1.6 TheAssumptionof‘Rationality’. . . . . . . . . . . . . . . . . . . . 31
2.1.7 SimultaneousandSequentialGame. . . . . . . . . . . . . . . . . . 32
2.2 GameTheoreticModelswithaDiscreteSetofStrategies. . . . . . . 33
2.2.1 DiscreteandContinuousSetofStrategies. . . . . . . . . . . . . 33
2.2.2 NashEquilibrium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.2.3 StackelbergEquilibrium. . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.3 CriticismsonGameTheoreticModelsforSecurity
Improvement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.4 IntegratingConventionalSecurityRiskAssessment
MethodologiesandGameTheoryforImprovingChemical
PlantProtection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3 SinglePlantProtection:AGame-TheoreticalModel
forImprovingChemicalPlantProtection. . . . . . . . . . . . . . . . . . . . . 43
3.1 GeneralIntrusionDetectionApproachinChemicalPlants. . . . . . . 43
3.2 Game-TheoreticalModelling:TheChemicalPlant
ProtectionGame(CPPGame). . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.1 Players. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.2 Strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.2.3 Payoffs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.3 SolutionsfortheCPPGame. . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3.1 NashEquilibrium. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3.2 StackelbergEquilibrium. . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3.3 BayesianNashEquilibrium. . . . . . . . . . . . . . . . . . . . . . . 55
3.3.4 BayesianStackelbergEquilibrium. . . . . . . . . . . . . . . . . . . 56
3.4 CPPGamefromanIndustrialPracticePointofView. . . . . . . . . . 58
3.4.1 InputAnalysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4.2 OutputAnalysis. . .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . 62
3.5 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4 SinglePlantProtection:PlayingtheChemicalPlant
ProtectionGamewithDistribution-FreeUncertainties. . . . . . . . . . . 65
4.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2 IntervalCPPGameDefinition. . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3 IntervalBi-MatrixGameSolver(IBGS). . . . . . . . . . . . . . . . . . . . 67
4.4 ParameterCoupling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.5 IntervalCPPGameSolver(ICGS). . . . . . . . . . . . . . . . . . . . . . . . 74
4.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77