Dynamic Games in Cyber-Physical Security: An Overview

Abstract

Due to complex dependencies between multiple layers and components of emerging cyber-physical systems, security and vulnerability of such systems have become a major challenge in recent years. In this regard, game theory, a powerful tool for modeling strategic interactions between multiple decision makers with conflicting objectives, offers a natural paradigm to address the security-related issues arising in these systems. While there exists substantial amount of work in modeling and analyzing security problems using game-theoretic techniques, most of the existing literature in this area focuses on static game models, ignoring the dynamic nature of interactions between the main players (defenders vs. attackers). In this paper, we focus only on dynamic game analysis of cyber-physical security problems and provide a general overview of the existing results and recent advances based on application domains. We also discuss several limitations of the existing models and identify several hitherto unaddressed directions for future research.

This is a preview of subscription content, log in to check access.

Notes

  1. 1.

    A strategy can be either pure or mixed, meaning that a player can either choose a particular action with probability 1, or based on a probability distribution over its set of possible actions.

  2. 2.

    A perfect Bayesian equilibrium is a set of strategies and beliefs for every player at every information set, so that the beliefs are derived from the strategies and common prior beliefs using Bayes’ rule, and the strategies are optimal at every point in the game, given the players’ beliefs.

  3. 3.

    Fictitious play is a learning rule in which at each round, each player best responds to the empirical frequency of play of his opponents.

  4. 4.

    Colonel Blotto game is a multi-dimensional problem on strategic resource allocation. In its classic form, it is a two-player game in which two colonels are tasked with allocating a limited number of troops over multiple battlefields, with the player allocating the most troops to a front being declared the winner, and the overall payoff being proportional to the number of fronts won.

  5. 5.

    Pursuit–evasion games model many security problems where one or more evaders try to escape a group of pursuing units; see [21].

  6. 6.

    Incentive compatibility means that agents have no incentive to misreport their safety states, while individual rationality implies that agents voluntarily have incentives to participate in the mechanism.

  7. 7.

    In an information system, the system’s overall security usually depends on its weakest link.

  8. 8.

    This is a strategy that yields the highest total payoff for that player if he knows the entire sequence of attacks a priori.

References

  1. 1.

    Acquisti A, Dingledine R, Syverson P (2003) On the economics of anonymity. In: International conference on financial cryptography. Springer, pp 84–102

  2. 2.

    Akyol E, Rose K, Başar T (2015) Optimal zero-delay jamming over an additive noise channel. IEEE Trans Inf Theory IT–61:4331–4344

    MathSciNet  MATH  Google Scholar 

  3. 3.

    Akyol E, Rose K, Başar T (2013) On communication over Gaussian sensor networks with adversaries: further results. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 1–9

  4. 4.

    Alpcan T, Başar T (2011) Network security: a decision and game theoretic approach. Cambridge University Press, Cambridge

    Google Scholar 

  5. 5.

    Alpcan T, Başar T (2004) A game theoretic analysis of intrusion detection in access control systems. In: 43rd IEEE conference on decision and control (CDC), vol 2, pp 1568–1573

  6. 6.

    Alpcan T, Başar T (2006) An intrusion detection game with limited observations. In: 12th international symposium on dynamic games and applications, vol 26. Sophia Antipolis, France

  7. 7.

    Alpern S, Lidbetter T, Morton A, Papadaki K (2016) Patrolling a pipeline. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 129–138

  8. 8.

    Amin S, Schwartz GA, Hussain A (2013) In quest of benchmarking security risks to cyber-physical systems. IEEE Netw 27(1):19–24

    Google Scholar 

  9. 9.

    An B, Brown M, Vorobeychik Y, Tambe M (2013) Security games with surveillance cost and optimal timing of attack execution In: Proceedings of the 2013 international conference on autonomous agents and multi-agent systems, pp 223–230

  10. 10.

    Anderson R, Moore T, Nagaraja S, Ozment A (2007) Incentives and information security. Algorithmic Game Theory, pp 633–649

  11. 11.

    Balcan M-F, Blum A, Haghtalab N, Procaccia AD (2015) Commitment without regrets: online learning in Stackelberg security games. In: Proceedings of the sixteenth ACM conference on economics and computation, pp 61–78

  12. 12.

    Bandyopadhyay T, Liu D, Mookerjee VS, Wilhite AW (2014) Dynamic competition in IT security: a differential games approach. Inf Syst Front 16(4):643–661

    Google Scholar 

  13. 13.

    Bansal R, Başar T (1989) Communication games with partially soft power constraints. J Optim Theory Appl 61(3):329–346

    MathSciNet  MATH  Google Scholar 

  14. 14.

    Basak A, Fang F, Nguyen TH, Kiekintveld C (2016) Combining graph contraction and strategy generation for green security games. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 251–271

  15. 15.

    Başar T (1983) The Gaussian test channel with an intelligent jammer. IEEE Trans Inf Theory IT–29(1):152–157

    MATH  Google Scholar 

  16. 16.

    Başar TÜ, Başar T (1982) Optimum coding and decoding schemes for the transmission of a stochastic process over a continuous-time stochastic channel with partially unknown statistics. Stochastics 8(3):213–237

    MathSciNet  MATH  Google Scholar 

  17. 17.

    Başar T, Başar TÜ (1984) A bandwidth expanding scheme for communication channels with noiseless feedback and in the presence of unknown jamming noise. J Frankl Inst 317(2):73–88

    MathSciNet  MATH  Google Scholar 

  18. 18.

    Başar TÜ, Başar T (1989) Optimum linear causal coding schemes for Gaussian stochastic processes in the presence of correlated jamming. IEEE Trans Inf Theory 35(1):199–202

    MathSciNet  MATH  Google Scholar 

  19. 19.

    Başar T, Wu YW (1985) A complete characterization of minimax and maximin encoder-decoder policies for communication channels with incomplete statistical description. IEEE Trans Inf Theory IT–31(4):482–489

    MathSciNet  MATH  Google Scholar 

  20. 20.

    Başar T, Wu YW (1986) Solutions to a class of minimax decision problems arising in communication systems. J Optim Theory Appl 51(3):375–404

    MathSciNet  MATH  Google Scholar 

  21. 21.

    Başar T, Olsder GJ (1999) Dynamic noncooperative game theory. Series in classics in applied mathematics, SIAM, Philadelphia

  22. 22.

    Behrens DA, Caulkins JP, Feichtinger G, Tragler G (2007) Incentive Stackelberg strategies for a dynamic game on terrorism. Adv Dyn Game Theory, pp 459–486

  23. 23.

    Bensoussan A, Kantarcioglu M, Hoe S (2010) A game-theoretical approach for finding optimal strategies in a Botnet defense model. In: Proceedings of international conference on decision and game theory for security (GameSec), pp 135–148

    Google Scholar 

  24. 24.

    Bhattacharya S, Gupta A, Başar T (2013) Jamming in mobile networks: a game-theoretic approach. J. Numer Algebra Control Optim 3(1):1–30

    MathSciNet  MATH  Google Scholar 

  25. 25.

    Bhattacharya S, Başar T (2010) Game-theoretic analysis of an aerial jamming attack on a UAV communication network. In: American control conference (ACC). IEEE, pp 818–823

  26. 26.

    Bhattacharya S, Başar T (2011) Spatial approaches to broadband jamming in heterogeneous mobile networks: a game-theoretic approach. J Autonomous Robots

  27. 27.

    Bloem M, Alpcan T, Başar T (2006) Intrusion response as a resource allocation problem. In: 45th IEEE conference on decision and control, pp 6283–6288

  28. 28.

    Böhme R, Moore T (2016) The “iterated weakest link” model of adaptive security investment. J Inf Secur 7(02):81

    Google Scholar 

  29. 29.

    Bommannavar P, Alpcan T, Bambos N (2011) Security risk management via dynamic games with learning. In: IEEE international conference on communications (ICC), pp 1–6

  30. 30.

    Carroll TE, Grosu D (2011) A game theoretic investigation of deception in network security. Secur Commun Netw 4(10):1162–1172

    Google Scholar 

  31. 31.

    Casey W, Morales JA, Nguyen T, Spring J, Weaver R, Wright E, Metcalf L, Mishra B (2014) Cyber security via signaling games: toward a science of cyber security. In: International conference on distributed computing and internet technology. Springer, pp 34–42

  32. 32.

    Casey W, Weaver R, Metcalf L, Morales JA, Wright E, Mishra B (2014) Cyber security via minority games with epistatic signaling. In: Proceedings of the 8th international conference on bioinspired information and communications technologies, pp 133–140

  33. 33.

    Cavallo R (2008) Efficiency and redistribution in dynamic mechanism design. In: Proceedings of the 9th ACM conference on electronic commerce, pp 220–229

  34. 34.

    Çeker H, Zhuang J, Upadhyaya S, La QD, Soong B-H (2016) Deception-based game theoretical approach to mitigate DoS attacks. In: International conference on decision and game theory for security. Springer, pp 18–38

  35. 35.

    Chen J, Zhu Q (2016) Optimal contract design under asymmetric information for cloud-enabled internet of controlled things. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 329–348

  36. 36.

    Chicoisne R, Ordóñez F (2016) Risk averse Stackelberg security games with quantal response. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 83–100

  37. 37.

    Chung K, Kamhoua CA, Kwiat KA, Kalbarczyk ZT, Iyer RK (2016) Game theory with learning for cyber security monitoring. In: 17th IEEE international symposium on high assurance systems engineering (HASE), pp 1–8

  38. 38.

    Culnane C, Teague V (2016) Strategies for voter-initiated election audits. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 235–247

  39. 39.

    Dziubiński M, Sankowski P, Zhang Q (2016) Network elicitation in adversarial environment. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 397–414

  40. 40.

    Fang F, Stone P, Tambe M (2015) When security games go green: designing defender strategies to prevent poaching and illegal fishing. In: IJCAI, pp 2589–2595

  41. 41.

    Fang H, Xu L, Wang X (2017) Coordinated multiple-relays based physical-layer security improvement: a single-leader multiple-followers Stackelberg game scheme. In: IEEE transactions on information forensics and security, pp 75–80

  42. 42.

    Farhadi F, Tavafoghi H, Teneketzis D, Golestani J (2017) A dynamic incentive mechanism for security in networks of interdependent agents. In: 7th EAI international conference on game theory for networks

  43. 43.

    Farhang S, Grossklags J (2016) Flipleakage: a game-theoretic approach to protect against stealthy attackers in the presence of information leakage. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 195–214

  44. 44.

    Farhang S, Manshaei MH, Esfahani MN, Zhu Q (2014) A dynamic Bayesian security game framework for strategic defense mechanism design. In: International conference on decision and game theory for security. Springer, pp 319–328

  45. 45.

    Ghafouri A, Abbas W, Laszka A, Vorobeychik Y, Koutsoukos X (2016) Optimal thresholds for anomaly-based intrusion detection in dynamical environments. In: International conference on decision and game theory for security. Springer, pp 415–434

  46. 46.

    Gibson AS (2013) Applied hypergame theory for network defense. Air Force Inst of Tech WRIGHT-PATTERSON AFB OH Graduate School of Engineering Management. Tech. Rep

  47. 47.

    Gravin N, Peres Y, Sivan B (2016) Towards optimal algorithms for prediction with expert advice. In: Proceedings of the twenty-seventh annual ACM-SIAM symposium on discrete algorithms, pp 528–547

  48. 48.

    Gupta A, Langbort C, Başar T (2017) Dynamic games with asymmetric information and resource constrained players with applications to security of cyberphysical systems. IEEE Trans Control Netw Syst 4(1):71–81

    MathSciNet  MATH  Google Scholar 

  49. 49.

    Gupta A, Langbort C, Başar T (2010) Optimal control in the presence of an intelligent jammer with limited actions. In: 49th IEEE conference on decision and control (CDC), pp 1096–1101

  50. 50.

    Gupta A, Schwartz G, Langbort C, Sastry SS, Başar T (2014) A three-stage Colonel Blotto game with applications to cyber-physical security. In: Proceedings of IEEE American control conference (ACC), pp 3820–3825

  51. 51.

    He X, Dai H, Ning P (2015) Improving learning and adaptation in security games by exploiting information asymmetry. In: IEEE conference on computer communications (INFOCOM), pp 1787–1795

  52. 52.

    Horák K, Bošanskỳ B (2016) A point-based approximate algorithm for one-sided partially observable pursuit-evasion games. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 435–454

  53. 53.

    House JT, Cybenko G (2010) Hypergame theory applied to cyber attack and defense. Sens Command Control Commun Intell (C3I) Technol Homel Secur Homel Def IX 7666:766604–766611

    Google Scholar 

  54. 54.

    Jiang AX, Yin Z, Zhang C, Tambe M, Kraus S (2013) Game-theoretic randomization for security patrolling with dynamic execution uncertainty. In: Proceedings of the 2013 international conference on autonomous agents and multi-agent systems, pp 207–214

  55. 55.

    Jiang L, Anantharam V, Walrand J (2011) How bad are selfish investments in network security? IEEE/ACM Trans Netw 19(2):549–560

    Google Scholar 

  56. 56.

    Jones MG (2013) Asymmetric information games and cyber security. Ph.D. dissertation, Georgia Institute of Technology

  57. 57.

    Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:263–291

    MathSciNet  MATH  Google Scholar 

  58. 58.

    Kar D, Fang F, Delle Fave FM, Sintov N, Tambe M, Lyet A (2016) Comparing human behavior models in repeated Stackelberg security games: an extended study. Artif Intell 240:65–103

    MathSciNet  MATH  Google Scholar 

  59. 59.

    Kar D, Fang F, Delle Fave F, Sintov N, Tambe M (2015) A game of thrones: when human behavior models compete in repeated Stackelberg security games. In: Proceedings of the 2015 international conference on autonomous agents and multiagent systems, pp 1381–1390

  60. 60.

    Khouzani M, Sarkar S, Altman E (2012) Saddle-point strategies in malware attack. IEEE J Sel Areas Commun 30(1):31–43

    Google Scholar 

  61. 61.

    Kiekintveld C, Jain M, Tsai J, Pita J, Ordóñez F, Tambe M (2009) Computing optimal randomized resource allocations for massive security games. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems, vol 1, pp 689–696

  62. 62.

    La QD, Quek TQ, Lee J (2016) A game theoretic model for enabling honeypots in IoT networks. In: IEEE international conference on communications (ICC), pp 1–6

  63. 63.

    Laraki R, Sorin S (2015) Advances in zero-sum dynamic games. Handbook of game theory with economic applications 4:27–93

    Google Scholar 

  64. 64.

    Law YW, Alpcan T, Palaniswami M (2015) Security games for risk minimization in automatic generation control. IEEE Trans Power Syst 30(1):223–232

    Google Scholar 

  65. 65.

    Li Y, Shi L, Cheng P, Chen J, Quevedo DE (2015) Jamming attacks on remote state estimation in cyber-physical systems: a game-theoretic approach. IEEE Trans Autom Control 60(10):2831–2836

    MathSciNet  MATH  Google Scholar 

  66. 66.

    Liang X, Xiao Y (2013) Game theory for network security. IEEE Commun Surv Tutor 15(1):472–486

    Google Scholar 

  67. 67.

    Li H, Lai L, Qiu RC (2011) A denial-of-service jamming game for remote state monitoring in smart grid. In: 45th annual conference on information sciences and systems (CISS). IEEE, pp 1–6

  68. 68.

    Li L, Langbort C, Shamma J (2017) Computing security strategies in finite horizon repeated bayesian games. In: American control conference (ACC), pp 3664–3669

  69. 69.

    Lin J, Liu P, Jing J (2012) Using signaling games to model the multi-step attack-defense scenarios on confidentiality. In: Proceedings of conference on decision and game theory for security (GameSec). Springer, pp 118–137

  70. 70.

    Liu P, Zang W, Yu M (2005) Incentive-based modeling and inference of attacker intent, objectives, and strategies. ACM Trans Inf Syst Secur (TISSEC) 8(1):78–118

    Google Scholar 

  71. 71.

    Liu Y, Comaniciu C, Man H (2006) A Bayesian game approach for intrusion detection in wireless ad hoc networks. In: Proceedings of the 2006 workshop on game theory for communications and networks. ACM, p 4

  72. 72.

    Luo Y, Szidarovszky F, Al-Nashif Y, Hariri S (2010) Game theory based network security. Executive Editor in Chief

  73. 73.

    Lye K-W, Wing JM (2005) Game strategies in network security. Int J Inf Secur 4(1–2):71–86

    Google Scholar 

  74. 74.

    Ma J, Liu Y, Song L, Han Z (2015) Multiact dynamic game strategy for jamming attack in electricity market. IEEE Trans Smart Grid 6(5):2273–2282

    Google Scholar 

  75. 75.

    Mallik RK, Scholtz RA, Papavassilopoulos GP (2000) Analysis of an on–off jamming situation as a dynamic game. IEEE Trans Commun 48(8):1360–1373

    Google Scholar 

  76. 76.

    Manshaei MH, Zhu Q, Alpcan T, Başar T, Hubaux J-P (2013) Game theory meets network security and privacy. ACM Comput Surv (CSUR) 45(3):25:1–25:39

    MATH  Google Scholar 

  77. 77.

    Miao F, Pajic M, Pappas GJ (2013) Stochastic game approach for replay attack detection. In: IEEE 52nd annual conference on decision and control (CDC), pp 1854–1859

  78. 78.

    Miura-Ko RA, Yolken B, Bambos N, Mitchell J (2008) Security investment games of interdependent organizations. In: 46th annual Allerton conference on communication, control, and computing. IEEE, pp 252–260

  79. 79.

    Mohammadi A, Manshaei MH, Moghaddam MM, Zhu Q (2016) A game-theoretic analysis of deception over social networks using fake avatars. In: International conference on decision and game theory for security. Springer, pp 382–394

  80. 80.

    Mukherjee A, Swindlehurst AL (2013) Jamming games in the mimo wiretap channel with an active eavesdropper. IEEE Trans Sig Process 61(1):82–91

    MathSciNet  MATH  Google Scholar 

  81. 81.

    Mukhopadhyay A, Zhang C, Vorobeychik Y, Tambe M, Pence K, Speer P (2016) Optimal allocation of police patrol resources using a continuous-time crime model. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 139–158

  82. 82.

    Nadendla VSS, Akyol E, Langbort C, Başar T (2017) Strategic communication between prospect theoretic agents over a Gaussian test channel. In: Proceedings of MILCOM 2017, Baltimore, MD, Oct 23–25 (to appear)

  83. 83.

    Naghizadeh P, Liu M (2016) Inter-temporal incentives in security information sharing agreements. In: Information theory and applications workshop (ITA). IEEE, pp 1–8

  84. 84.

    Nguyen KC, Alpcan T, Başar T (2008) Fictitious play with imperfect observations for network intrusion detection. Preprints of the 13th international symposium dynamic games and applications (ISDGA), June 30–July 3, Wroclaw, Poland

  85. 85.

    Nguyen KC, Alpcan T, Başar T (2009) Security games with incomplete information. In: IEEE international conference on communications, ICC’09, pp 1–6

  86. 86.

    Nguyen KC, Alpcan T, Başar T (2009) Stochastic games for security in networks with interdependent nodes. In: International conference on game theory for networks. IEEE, pp 697–703

  87. 87.

    Nguyen TH, Yadav A, An B, Tambe M, Boutilier C (2014) Regret-based optimization and preference elicitation for Stackelberg security games with uncertainty. In: AAAI, pp 756–762

  88. 88.

    Noureddine MA, Fawaz A, Sanders WH, Başar T (2016) A game-theoretic approach to respond to attacker lateral movement. In: International conference on decision and game theory for security. Springer, pp 294–313

  89. 89.

    Ouyang Y, Tavafoghi H, Teneketzis D (2017) Dynamic games with asymmetric information: common information based perfect bayesian equilibria and sequential decomposition. IEEE Trans Autom Control 62(1):222–237

    MathSciNet  MATH  Google Scholar 

  90. 90.

    Patcha A, Park J-M (2006) A game theoretic formulation for intrusion detection in mobile ad hoc networks. IJ Netw Secur 2(2):131–137

    Google Scholar 

  91. 91.

    Pawlick J, Zhu Q (2015) Deception by design: evidence-based signaling games for network defense. arXiv preprint arXiv:1503.05458

  92. 92.

    Pita J, Tambe M, Kiekintveld C, Cullen S, Steigerwald E (2011) GUARDS: game theoretic security allocation on a national scale. In: The 10th international conference on autonomous agents and multiagent systems, vol 1, pp 37–44

  93. 93.

    Ramachandran K, Stefanova Z (2016) Dynamic game theories in cyber security. In: Proceedings of dynamic systems and applications, pp 1–8

  94. 94.

    Raya M, Manshaei MH, Félegyházi M, Hubaux J-P (2008) Revocation games in ephemeral networks. In: Proceedings of the 15th ACM conference on computer and communications security, pp 199–210

  95. 95.

    Raya M, Shokri R, Hubaux J-P (2010) “On the tradeoff between trust and privacy in wireless ad hoc networks. In: Proceedings of the third ACM conference on wireless network security, pp 75–80

  96. 96.

    Roy S, Ellis C, Shiva S, Dasgupta D, Shandilya V, Wu Q (2010) A survey of game theory as applied to network security. In: 43rd Hawaii international conference on system sciences (HICSS). IEEE, pp 1–10

  97. 97.

    Saad W, Zhou X, Maham B, Başar T, Poor HV (2012) Tree formation with physical layer security considerations in wireless multi-hop networks. IEEE Trans Wirel Commun 11(11):3980–3991

    Google Scholar 

  98. 98.

    Sagduyu YE, Berry RA, Ephremides A (2011) Jamming games in wireless networks with incomplete information. IEEE Commun Mag, 49(8)

    Google Scholar 

  99. 99.

    Sagduyu YE, Berry R, Ephremides A (2009) MAC games for distributed wireless network security with incomplete information of selfish and malicious user types. In: Proceedings of international conference on game theory for networks. IEEE, pp 130–139

  100. 100.

    Sallhammar K, Helvik BE, Knapskog SJ (2006) On stochastic modeling for integrated security and dependability evaluation. JNW 1(5):31–42

    Google Scholar 

  101. 101.

    Sallhammar K, Knapskog SJ, Helvik BE (2005) Using stochastic game theory to compute the expected behavior of attackers. In: 2005 symposium on applications and the internet workshops, saint workshops. IEEE, pp 102–105

  102. 102.

    Sanjab A, Saad W, Başar T (2017) Prospect theory for enhanced cyber-physical security of drone delivery systems: a network interdiction game. In: Proceedings of IEEE ICC 2017 communication and information security symposium (CISS 2017), Paris, France, May 21–25

  103. 103.

    Shelar D, Amin S (2017) Security assessment of electricity distribution networks under DER node compromises. IEEE Trans Control Netw Syst 4(1):23–36

    MathSciNet  MATH  Google Scholar 

  104. 104.

    Shen D, Chen G, Blasch E, Tadda G (2007) Adaptive Markov game theoretic data fusion approach for cyber network defense. In: Military communications conference (MILCOM). IEEE, pp 1–7

  105. 105.

    Sorin S (2011) Zero-sum repeated games: recent advances and new links with differential games. Dynamic games and applications 1(1):172–207

    MathSciNet  MATH  Google Scholar 

  106. 106.

    Trejo KK, Clempner JB, Poznyak AS (2016) Adapting strategies to dynamic environments in controllable Stackelberg security games. In: Proceedings of IEEE 55th conference on decision and control (CDC), pp 5484–5489

  107. 107.

    Truong A, Etesami SR, Etesami J, Kiyavash N (2018) Optimal attack strategies against predictors-learning from expert advice. IEEE Transactions on Information Forensics and Security

  108. 108.

    Wang S, Shroff N (2017) Security game with non-additive utilities and multiple attacker resources. Proc ACM Measur Anal Comput Syst 1(1):13

    Google Scholar 

  109. 109.

    Wang Y, Wang Y, Liu J, Huang Z, Xie P (2016) A survey of game theoretic methods for cyber security. In: IEEE international conference on data science in cyberspace (DSC), pp 631–636

  110. 110.

    Xiaolin C, Xiaobin T, Yong Z, Hongsheng X (2008) A Markov game theory-based risk assessment model for network information system. In: International conference on computer science and software engineering, vol 3. IEEE, pp 1057–1061

  111. 111.

    Zhang N, Yu W, Fu X, Das SK (2010) gPath: a game-theoretic path selection algorithm to protect Tors anonymity. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 58–71

  112. 112.

    Zheng J, Castañón DA (2012) Dynamic network interdiction games with imperfect information and deception. In: IEEE 51st annual conference on decision and control (CDC), pp 7758–7763

  113. 113.

    Zheng J, Castanón DA (2012) Stochastic dynamic network interdiction games. In: Proceedings of IEEE American control conference (ACC), pp 1838–1844

  114. 114.

    Zhou Z, Bambos N, Glynn P (2016) Dynamics on linear influence network games under stochastic environments. In: Proceedings of international conference on decision and game theory for security (GameSec). Springer, pp 114–126

  115. 115.

    Zhu Q, Başar T (2015) Game-theoretic methods for robustness, security, and resilience of cyberphysical control systems: games-in-games principle for optimal cross-layer resilient control systems. IEEE Control Syst 35(1):46–65

    MathSciNet  Google Scholar 

  116. 116.

    Zhu Q, Fung C, Boutaba R, Başar T (2012) GUIDEX: a game-theoretic incentive-based mechanism for intrusion detection networks. IEEE J Select Areas Commun (JSAC) Spec Iss Econ Commun Netw Syst (SI-NetEcon) 30(11):2220–2230

    Google Scholar 

  117. 117.

    Zhu Q, Başar T (2009) Dynamic policy-based IDS configuration. In: Proceedings of the 48th IEEE conference on decision and control (CDC), pp 8600–8605

  118. 118.

    Zhu Q, Başar T (2012) A dynamic game-theoretic approach to resilient control system design for cascading failures. In: Proceedings of the 1st international conference on high confidence networked systems. ACM, pp 41–46

  119. 119.

    Zhu Q, Clark A, Poovendran R, Başar T (2012) Deceptive routing games. In: IEEE 51st annual conference on decision and control (CDC), pp 2704–2711

  120. 120.

    Zhu Q, Tembine H, Başar T (2010) Heterogeneous learning in zero-sum stochastic games with incomplete information. In: 49th IEEE conference on decision and control (CDC), pp 219–224

  121. 121.

    Zhu Q, Tembine H, Başar T (2010) Network security configurations: a nonzero-sum stochastic game approach. In: IEEE American control conference (ACC), pp 1059–1064

  122. 122.

    Zhu Q, Tembine H, Başar T (2013) Hybrid learning in stochastic games and its applications in network security. In: Lewis FL, Liu D (eds) Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, Series on Computational Intelligence, IEEE Press/Wiley, chapter 14, pp 305–329

  123. 123.

    Zonouz SA, Khurana H, Sanders WH, Yardley TM (2014) RRE: a game-theoretic intrusion response and recovery engine. IEEE Trans Parallel Distrib Syst 25(2):395–406

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to S. Rasoul Etesami.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The research leading to this paper was supported in part by ONR MURI Grant N00014-16-1-2710 and in part by ARO Grant W911NF-16-1-0485.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Etesami, S.R., Başar, T. Dynamic Games in Cyber-Physical Security: An Overview. Dyn Games Appl 9, 884–913 (2019). https://doi.org/10.1007/s13235-018-00291-y

Download citation

Keywords

  • Dynamic game
  • Cyber-physical security
  • Network security
  • Mechanism design
  • Learning
  • Security game