Providing a model and solution method for fuzzy security games and its application in future studies on security threats

Document Type : Original Article

Authors

1 Researcher, Institute for the Study of War, army Command and Staff University,Tehran, I.R.Iran.

2 Department of Industrial Engineering, Faculty of Industrial and Computer Engineering,Birjand University of Technology, Birjand, I.R. Iran.

Abstract

Global threats of terrorism, drug-smuggling, and other crimes cause to increase the need to deploy limited security resources to maximize their effectiveness. Prediction of future actions and reviewing all the possible strategies of the attackers has a great influence on the success in the battle. Game theory is one of the techniques of futures studies. This paper examines security games in a fuzzy environment. The security games consider encounter model of a defender and several types of attackers. The aim of this paper was providing a method to compute optimal strategy of defender in the conditions of uncertainty. Modeling security threats of centers of public transportation was described. The Bayesian approach was used to solve the uncertainty of encounter a defender with unknown attacker type, and fuzzy theory was used to resolve the uncertainty issue due to ambiguity in understanding the experts and their inadequate judgment. After presenting the model, the problem was written as crisp using α-cuts of fuzzy numbers. Finally, the obtained model was reviewed and its role in the future studies of security threats was addressed, and the validity of the proposed method for an applied sample was examined.

Keywords

References

  • An, B. Tambe, M. Ordonez, F. & Shieh, E. (2011). Refinement of Strong Stackelberg Equilibria in Security Games, Association for the Advancement of Artificial Intelligence. 
  • Bazaraa M.S.  & ‎Jarvis J.J. (1997). ‎‎‎Linear Programming and Network Flows, John Wiley & Sons‎, ‎Inc: ‎NewYork ‎.
  • Bigdeli, H. & Hassanpour, H. (2018). Modeling and solving multiobjective security game problem using multiobjective bilevel problem and its application in metro security system.
  • Bigdeli, H. Hassanpour H. & Tayyebi, J. (2016). The optimistic and pessimistic solutions of single and multiobjective matrix games with fuzzy payoffs and analysis of some of military problems, Defence Sci & Tech, Acceptd, (In Persian).
  • Bigdeli, H. Hassanpour, H. & Tayyebi, J. (2018). ‎‎Constrained Bimatrix Games with Fuzzy Goals and its Application in Nuclear Negotiations.
  • Bigdeli, H. Hassanpour, H., & Tayyebi, J. (2018). Multiobjective security game with fuzzy payoffs, IJFS.
  • Bigdeli, H., Hassanpour, H. (2016). A satisfactory strategy of multiobjective two person matrix games with fuzzy payoffs, Iranian Journal of Fuzzy Systems, 13: 17-33.
  • Brown, G., Carlyle, M., Kline, J. & Wood, K. (2005). ‎‎A Two-Sided Optimization for Theater Ballistic Missile Defense, In Operations Research, 53: 263–275‎.
  • Brown, M. An, B.  Kiekintveld, C. Ordóñez, F. &  Tambe, M. (2014). ‎‎An extended study on multi-objective security games, Auton Agent Multi-Agent Syst, 28: 31–71.
  • Gatti, N. (2008). Game Theoretical Insights in Strategic Patrolling: Model and Algorithm in Normal-Form, in ECAI-08, pp. 403–407‎.
  • Haywood, O‎. ‎G‎. (1989). ‎‎Military Decision and Game Theory, Wiley‎, ‎‎Journal of the Operations Research Society of America‎, 2 (4): 365-385‎.
  • Kheirkhah, A. S. Navidi, H. R. Bidgoli, M. M. (2017). Modeling and Solving the Hazmat Routing Problem under Network Interdiction with Information Asymmetry, Journal of transportation engineering, 9 (1): 17-36.
  • Lye, K. & Wing, J. M. (2005). Game Strategies in Network Security, International Journal of Information Security, 4 (1–2): 71–86.
  • Neumann, J‎. ‎V‎. &‎ ‎Morgenstern, ‎O‎. (1944). Theory of Games and Economic Behavior, Wiley‎, ‎New York.
  • ‎Owen, G. (1995). ‎‎Game Theory, Academic Press‎, ‎San Diego‎, ‎Third Edition‎.
  • Sakawa, M. & Nishizaki, I. (2009). ‎‎Cooperative and Noncooperative Multi-Level Programming, Springer, New York and london.
  • Sakawa, M. (1993). ‎‎Fuzzy sets and interactive multiobjective optimization, Plenum press, New York and london.
  • Sandler, T. & D. G. A. M. (2003). ‎‎Terrorism and Game Theory, Simulation and Gaming, 34 (3): 319–337.
  • Tambe, M. (2012). ‎‎Security and game theory, algorithms, deployed systems, lessons learned, Cambridge university press‎.
Defensive Future Studies
Volume 2, Issue 6 - Serial Number 6
December 2017
Pages 7-29

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