Cyber Warfare pp 81-101

Part of the Advances in Information Security book series (ADIS, volume 56) | Cite as

Game-Theoretic Foundations for the Strategic Use of Honeypots in Network Security

  • Christopher Kiekintveld
  • Viliam Lisý
  • Radek Píbil
Chapter

Abstract

An important element in the mathematical and scientific foundations for security is modeling the strategic use of deception and information manipulation. We argue that game theory provides an important theoretical framework for reasoning about information manipulation in adversarial settings, including deception and randomization strategies. In addition, game theory has practical uses in determining optimal strategies for randomized patrolling and resource allocation. We discuss three game-theoretic models that capture aspects of how honeypots can be used in network security. Honeypots are fake hosts introduced into a network to gather information about attackers and to distract them from real targets. They are a limited resource, so there are important strategic questions about how to deploy them to the greatest effect, which is fundamentally about deceiving attackers into choosing fake targets instead of real ones to attack. We describe several game models that address strategies for deploying honeypots, including a basic honeypot selection game, an extension of this game that allows additional probing actions by the attacker, and finally a version in which attacker strategies are represented using attack graphs. We conclude with a discussion of the strengths and limitations of game theory in the context of network security.

References

  1. B. Bosansky, C. Kiekintveld, V. Lisy, J. Cermak, and M. Pechoucek. Double-oracle algorithm for computing an exact nash equilibrium in zero-sum extensive-form games. InProceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), 2013Google Scholar
  2. A. Buldas and R. Stepanenko. Upper bounds for adversaries’ utility in attack trees. In J. Grossklags and J. Walrand, editors,Decision and Game Theory for Security, volume 7638 of Lecture Notes in Computer Science, pages 98–117. Springer Berlin Heidelberg, 2012.Google Scholar
  3. C. F. Camerer.Behavioral Game Theory: Experiments in Strategic Interaction. Princeton University Press, 2003.Google Scholar
  4. F. Cohen. A mathematical structure of simple defensive network deception.Computers & Security, 19(6):520–528, 2000.Google Scholar
  5. M. Dornseif and T. Holz. Nosebreak-attacking honeynets. In2004 IEEE Workshop on Information Assurance and Security, 2004.Google Scholar
  6. K. Durkota and V. Lisy. Computing optimal policies for attack graphs with action failures and costs. InSTAIRS 2014: Proceedings of the Seventh Starting Ai Researchers’ Symposium, page (to appear). IOS Press, 2014.Google Scholar
  7. D. Fudenberg and D. K. Levine.The Theory of Learning in Games. MIT Press, 1998.Google Scholar
  8. K. Ingols, R. Lippmann, and K. Piwowarski. Practical attack graph generation for network defense. InComputer Security Applications Conference, 2006. ACSAC'06. 22nd Annual, pages 121–130. IEEE, 2006.Google Scholar
  9. M. Jain, D. Korzhyk, O. Vanek, V. Conitzer, M. Pechoucek, and M. Tambe. A double oracle algorithm for zero-sum security games on graphs. InInternational Conference on Autonomous Agents and Multiagent Systems, 2011.Google Scholar
  10. G. S. Kc, A. D. Keromytis, and V. Prevelakis. Countering code-injection attacks with instruction-set randomization. InProceedings of the 10th ACM Conference on Computer and Communications Security, CCS '03, pages 272–280, New York, NY, USA, 2003. ACM.Google Scholar
  11. C. Kiekintveld.Empirical Game-Theoretic Methods for Strategy Design and Analysis in Complex Games. PhD thesis, Universit y of Michigan, 2008.Google Scholar
  12. C. Kiekintveld, M. Jain, J. Tsai, J. Pita, F. Ordonez, and M. Tambe. Computing optimal randomized resource allocations for massive security games. InAAMAS-09, 2009.Google Scholar
  13. K. Lee. On a deception game with three boxes.International Journal of Game Theory, 22(2):89–95, 1993.Google Scholar
  14. V. Lisy and R. Pibil. Computing optimal attack strategies using unconstrained influence diagrams. In G. Wang, X. Zheng, M. Chau, and H. Chen, editors,Intelligence and Security Informatics, volume 8039 of Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013.Google Scholar
  15. J. Nash. Non-cooperative games.The Annals of Mathematics, 54(2):286–295, 1951.Google Scholar
  16. M. J. Osborne.An Introduction to Game Theory. Oxford University Press, 2004.Google Scholar
  17. X. Ou, W. Boyer, and M. McQueen. A scalable approach to attack graph generation. InProceedings of the 13th ACM conference on Computer and communications security, pages 336–345. ACM, 2006.Google Scholar
  18. R. Pibil, V. Lisy, C. Kiekintveld, B. Bosansky, and M. Pechoucek. Game theoretic model of strategic honeypot selection in computer networks. In J. Grossklags and J. Walrand, editors,Decision and Game Theory for Security, volume 7638 ofLecture Notes in Computer Science. Springer Berlin Heidelberg, 2012.Google Scholar
  19. J. Pita, M. Jain, C. Western, C. Portway, M. Tambe, F. Ordonez, S. Kraus, and P. Parachuri. Depoloyed ARMOR protection: The application of a game-theoretic model for security at the Los Angeles International Airport. InAAMAS-08 (Industry Track), 2008.Google Scholar
  20. J. Pita, M. Tambe, C. Kiekintveld, S. Cullen, and E. Steigerwald. GUARDS—game theoretic security allocation on a national scale. InAAMAS-11 (Industry Track), 2011.Google Scholar
  21. N. C. Rowe, E. J. Custy, and B. T. Duong. Defending Cyberspace with Fake Honeypots.Journal of Computers, 2(2):25–36, Apr. 2007.Google Scholar
  22. T. Sandholm. The state of solving large incomplete-information games, and application to poker.AI Magazine, Special Issue on Algorithmic Game Theory, 2010.Google Scholar
  23. T. Sandholm and S. Singh. Lossy stochastic game abstraction with bounds. InACM Conference on Electronic Commerce (EC), 2012.Google Scholar
  24. E. Shieh, B. An, R. Yang, M. Tambe, C. Baldwin, J. Direnzo, G. Meyer, C. W. Baldwin, B. J. Maule, and G. R. Meyer. PROTECT: A Deployed Game Theoretic System to Protect the Ports of the United States.AAMAS, 2012.Google Scholar
  25. Y. Shoham and K. Leyton-Brown.Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, 2009.Google Scholar
  26. J. Spencer. A deception game.American Mathematical Monthly, page 416–417, 1973.Google Scholar
  27. L. Spitzner.Honeypots: tracking hackers. Addison-Wesley Professional, 2003.Google Scholar
  28. M. Tambe.Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned. Cambridge University Press, 2011.Google Scholar
  29. J. Tsai, Y. Qian, Y. Vorobeychik, C. Kiekintveld, and M. Tambe. Bayesian security games for controlling contagion. InIn Proceedings of the ASE/IEEE International Conference on Social Computing(SocialCom), 2013.Google Scholar
  30. J. Tsai, S. Rathi, C. Kiekintveld, F. Ordó nez, and M. Tambe. IRIS—A tools for strategic security allocation in transportation networks. InAAMAS-09 (Industry Track), 2009.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Christopher Kiekintveld
    • 1
  • Viliam Lisý
    • 2
  • Radek Píbil
    • 2
  1. 1.Computer Science DepartmentUniversity of Texas at El PasoEl PasoUSA
  2. 2.Agent Technology Center, Department of Computer Science and Engineering, Faculty of Electrical EngineeringCzech Technical University in PraguePragueCzech Republic

Personalised recommendations