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Security from the Adversary’s Inertia–Controlling Convergence Speed When Playing Mixed Strategy Equilibria

Author

Listed:
  • Jasmin Wachter

    (System Security Group, Institute of Applied Informatics, Alpen-Adria-Universität Klagenfurt, Universitätsstrasse 65-67, 9020 Klagenfurt, Austria)

  • Stefan Rass

    (System Security Group, Institute of Applied Informatics, Alpen-Adria-Universität Klagenfurt, Universitätsstrasse 65-67, 9020 Klagenfurt, Austria)

  • Sandra König

    (Center for Digital Safety & Security, Austrian Institute of Technology, Giefinggasse 4, 1210 Vienna, Austria)

Abstract

Game-theoretic models are a convenient tool to systematically analyze competitive situations. This makes them particularly handy in the field of security where a company or a critical infrastructure wants to defend against an attacker. When the optimal solution of the security game involves several pure strategies (i.e., the equilibrium is mixed), this may induce additional costs. Minimizing these costs can be done simultaneously with the original goal of minimizing the damage due to the attack. Existing models assume that the attacker instantly knows the action chosen by the defender (i.e., the pure strategy he is playing in the i -th round) but in real situations this may take some time. Such adversarial inertia can be exploited to gain security and save cost. To this end, we introduce the concept of information delay , which is defined as the time it takes an attacker to mount an attack. In this period it is assumed that the adversary has no information about the present state of the system, but only knows the last state before commencing the attack. Based on a Markov chain model we construct strategy policies that are cheaper in terms of maintenance (switching costs) when compared to classical approaches. The proposed approach yields slightly larger security risk but overall ensures a better performance. Furthermore, by reinvesting the saved costs in additional security measures it is possible to obtain even more security at the same overall cost.

Suggested Citation

  • Jasmin Wachter & Stefan Rass & Sandra König, 2018. "Security from the Adversary’s Inertia–Controlling Convergence Speed When Playing Mixed Strategy Equilibria," Games, MDPI, vol. 9(3), pages 1-15, August.
  • Handle: RePEc:gam:jgames:v:9:y:2018:i:3:p:59-:d:164848
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    References listed on IDEAS

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    1. Fudenberg, Drew & Levine, David, 1998. "Learning in games," European Economic Review, Elsevier, vol. 42(3-5), pages 631-639, May.
    2. Chien, Steve & Sinclair, Alistair, 2011. "Convergence to approximate Nash equilibria in congestion games," Games and Economic Behavior, Elsevier, vol. 71(2), pages 315-327, March.
    3. Vicki Bier & Santiago Oliveros & Larry Samuelson, 2007. "Choosing What to Protect: Strategic Defensive Allocation against an Unknown Attacker," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 9(4), pages 563-587, August.
    4. Drew Fudenberg & David K. Levine, 1998. "The Theory of Learning in Games," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262061945, December.
    5. Stuart, McDonald & Liam, Wagner, 2003. "Using Simulated Annealing to Calculate the Trembles of Trembling Hand Perfection," MPRA Paper 89127, University Library of Munich, Germany.
    6. Insua, Insua Rios & Rios, Jesus & Banks, David, 2009. "Adversarial Risk Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 841-854.
    7. Stuart McDonald, 2002. "Using Simulated Annealing to Compute the Trembles of Trembling Hand Perfection," Computing in Economics and Finance 2002 220, Society for Computational Economics.
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    Cited by:

    1. Yevgeny Tsodikovich & Xavier Venel & Anna Zseleva, 2021. "Repeated Games with Switching Costs: Stationary vs History-Independent Strategies," AMSE Working Papers 2129, Aix-Marseille School of Economics, France.
    2. Yevgeny Tsodikovich & Xavier Venel & Anna Zseleva, 2024. "The Price of History-Independent Strategies in Games with Inter-Temporal Externalities," Dynamic Games and Applications, Springer, vol. 14(5), pages 1317-1332, November.
    3. G. Liuzzi & M. Locatelli & V. Piccialli & S. Rass, 2021. "Computing mixed strategies equilibria in presence of switching costs by the solution of nonconvex QP problems," Computational Optimization and Applications, Springer, vol. 79(3), pages 561-599, July.
    4. Yevgeny Tsodikovich & Xavier Venel & Anna Zseleva, 2021. "Repeated Games with Switching Costs: Stationary vs History-Independent Strategies," Working Papers halshs-03223279, HAL.
    5. Yevgeny Tsodikovich & Xavier Venel & Anna Zseleva, 2021. "Repeated Games with Switching Costs: Stationary vs History Independent Strategies," Papers 2103.00045, arXiv.org, revised Oct 2021.

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