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Towards a bayesian network game framework for evaluating DDoS attacks and defense

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Published:16 October 2012Publication History

ABSTRACT

With a long history of compromising Internet security, Distributed Denial-of-Service (DDoS) attacks have been intensively investigated and numerous countermeasures have been proposed to defend against them. In this work, we propose a non-standard game-theoretic framework that facilitates evaluation of DDoS attacks and defense. Our framework can be used to study diverse DDoS attack scenarios where multiple layers of protection are deployed and a number of uncertain factors affect the decision making of the players, and it also allows us to model different sophistication levels of reasoning by both the attacker and the defender. We conduct a variety of experiments to evaluate DDoS attack and defense scenarios where one or more layers of defense mechanisms are deployed, and demonstrate that our framework sheds light on the interplay between decision makings of both the attacker and the defender, as well as how they affect the outcomes of DDoS attack and defense games.

References

  1. A. Arad and A. Rubinstein. The 11-20 money request game: Evaluating the upper bound of k-level reasoning. Technical report, Tel Aviv University Working Paper, May 2010.Google ScholarGoogle Scholar
  2. B. Bencsath, I. Vajda, and L. Buttyan. A game based analysis of the client puzzle approach to defend against DoS attacks. In Proceedings of the 2003 International Conference on Software, Telecommunications and Computer Networks, pages 763--767, 2003.Google ScholarGoogle Scholar
  3. C. F. Camerer. Behavioral game theory: experiments in strategic interaction. Princeton University Press, 2003.Google ScholarGoogle Scholar
  4. N. Christin, S. Egelman, T. Vidas, and J. Grossklags. It's all about the Benjamins: An empirical study on incentivizing users to ignore security advice. In Proceedings of IFCA Financial Cryptography'11, pages 16--30, Saint Lucia, February 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. http://edition.cnn.com/2008/TECH/04/18/cnn.websites/.Google ScholarGoogle Scholar
  6. M. Fallah. A puzzle-based defense strategy against flooding attacks using game theory. IEEE Transactions on Dependable And Secure Computing, 7:5--19, January 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. T. Khirwadkar, K. C. Nguyen, D. M. Nicol, and T. Basar. Methodologies for evaluating game theoretic defense against DDoS attacks. In Proceedings of the 2010 Winter Simulation Conference, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. I. Kotenko and A. Ulanov. Simulation of internet DDoS attacks and defense. In Proceedings of the 9th international conference on Information Security, ISC'06, pages 327--342, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. R. Lee and D. Wolpert. Game theoretic modeling of pilot behavior during mid-air encounters. In Decision Making with Imperfect Decision Makers, pages 75--111. Springer, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  11. Z. Li, Y. Xiang, and D. He. Computational intelligence and security. chapter Simulation and Analysis of DDoS in Active Defense Environment, pages 878--886. Springer-Verlag, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. R. Mahajan, S. M. Bellovin, S. Floyd, J. Ioannidis, V. Paxson, and S. Shenker. Controlling high bandwidth aggregates in the network. ACM SIGCOMM Computer Communication Review, 32:62--73, July 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. H. Manshaei, Q. Zhu, T. Alpcan, T. Basar, and J.-P. Hubaux. Game theory meets network security, 2010. Submitted to ACM Survey.Google ScholarGoogle Scholar
  14. J. Mirkovic and P. Reiher. A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Computer Communications Review, 34(2), April 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. Mirkovic, S. Wei, A. Hussain, B. Wilson, R. Thomas, S. Schwab, S. Fahmy, R. Chertov, and P. Reiner. DDoS benchmarks and experimenter's workbench for the deter testbed. In Proceedings of the 3rd International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities (TridentCom'07), pages 1--7, May 2007.Google ScholarGoogle ScholarCross RefCross Ref
  16. R. Nagel. Unraveling in guessing games: An experimental study. American Economic Review, 85(5):1313--26, December 1995.Google ScholarGoogle Scholar
  17. T. Peng, C. Leckie, and K. Ramamohanarao. Survey of network-based defense mechanisms countering the DoS and DDoS problems. ACM Computing Surveys, 39, April 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. Roy, C. Ellis, S. Shiva, D. Dasgupta, V. Shandilya, and Q. Wu. A survey of game theory as applied to network security. In Proceedings of the 2010 43rd Hawaii International Conference on System Sciences, pages 1--10, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. D. Schmidt, S. Suriadi, A. Tickle, A. Clark, G. Mohay, E. Ahmed, and J. Mackie. A distributed denial of service testbed. In Jacques Berleur, Magda Hercheui, and Lorenz Hilty, editors, What Kind of Information Society? Governance, Virtuality, Surveillance, Sustainability, Resilience, volume 328 of IFIP Advances in Information and Communication Technology. Springer Boston, 2010.Google ScholarGoogle Scholar
  20. P. Shi and Y. Lian. Game-theoretical effectiveness evaluation of DDoS defense. In Proceedings of the Seventh International Conference on Networking (ICN'08), pages 427 --433, April 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. H. A. Simon. Rational choice and the structure of the environment. Psychological Review, 63(2):129--138, 1956.Google ScholarGoogle ScholarCross RefCross Ref
  22. M. E. Snyder, R. Sundaram, and M. Thakur. A game-theoretic framework for bandwidth attacks and statistical defenses. In Proceedings of the 32nd IEEE Conference on Local Computer Networks, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. http://www.sans.org/security-resources/malwarefaq/stacheldraht.php.Google ScholarGoogle Scholar
  24. D. Stiliadis and A. Varma. Latency-rate servers: a general model for analysis of traffic scheduling algorithms. IEEE/ACM Transactions on Networking, 6(5):611--624, October 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. http://www.internetnews.com/security/article.php/3933046/How+Much+Does+a+DDoS+Attack+Cost.htm.Google ScholarGoogle Scholar
  26. http://www.pcmag.com/article2/0,2817,2374063,00.asp.Google ScholarGoogle Scholar
  27. Q. Wu, S. Shiva, S. Roy, C. Ellis, and V. Datla. On modeling and simulation of game theory-based defense mechanisms against DoS and DDoS attacks. In Proceedings of the 2010 Spring Simulation Multiconference, SpringSim '10, pages 159:1--159:8. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. J. Xu and W. Lee. Sustaining availability of web services under distributed denial of service attacks. IEEE Transactions on Computers, 52(2):195 -- 208, feb. 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. http://news.cnet.com/2100-1023-236621.html.Google ScholarGoogle Scholar
  30. G. Yan and S. Eidenbenz. DDoS mitigation in non-cooperative environments. In Proceedings of the 7th international IFIP-TC6 networking conference, NETWORKING'08, Singapore, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. W. Zang, P. Liu, and M. Yu. How resilient is the Internet against DDoS attacks? -- a game theoretic analysis of signature-based rate limiting. International Journal of Intelligent Control and Systems, 12(4):307--316, December 2007.Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Conferences
        CCS '12: Proceedings of the 2012 ACM conference on Computer and communications security
        October 2012
        1088 pages
        ISBN:9781450316514
        DOI:10.1145/2382196

        Copyright © 2012 ACM

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        Publication History

        • Published: 16 October 2012

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