A Learning-Based Approach to Reactive Security

  • Adam Barth
  • Benjamin I. P. Rubinstein
  • Mukund Sundararajan
  • John C. Mitchell
  • Dawn Song
  • Peter L. Bartlett
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6052)

Abstract

Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst-case assumptions about the attacker: we grant the attacker complete knowledge of the defender’s strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker’s incentives and knowledge.

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References

  1. 1.
    Anderson, R.: Why information security is hard—An economic perspective. In: 17th Annual Computer Security Applications Conference, pp. 358–365 (2001)Google Scholar
  2. 2.
    August, T., Tunca, T.I.: Network software security and user incentives. Management Science 52(11), 1703–1720 (2006)CrossRefGoogle Scholar
  3. 3.
    Barth, A., Rubinstein, B.I.P., Sundararajan, M., Mitchell, J.C., Song, D., Bartlett, P.L.: A learning-based approach to reactive security (2009), http://arxiv.org/abs/0912.1155
  4. 4.
    Beard, C.: Introducing Test Pilot (March 2008), http://labs.mozilla.com/2008/03/introducing-test-pilot/
  5. 5.
    Cavusoglu, H., Raghunathan, S., Yue, W.: Decision-theoretic and game-theoretic approaches to IT security investment. Journal of Management Information Systems 25(2), 281–304 (2008)CrossRefGoogle Scholar
  6. 6.
    Cesa-Bianchi, N., Freund, Y., Haussler, D., Helmbold, D.P., Schapire, R.E., Warmuth, M.K.: How to use expert advice. Journal of the Association for Computing Machinery 44(3), 427–485 (1997)MATHMathSciNetGoogle Scholar
  7. 7.
    Chakrabarty, D., Mehta, A., Vazirani, V.V.: Design is as easy as optimization. In: Bugliesi, M., Preneel, B., Sassone, V., Wegener, I. (eds.) ICALP 2006. LNCS, vol. 4051, pp. 477–488. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Cremonini, M.: Evaluating information security investments from attackers perspective: the return-on-attack (ROA). In: Fourth Workshop on the Economics of Information Security (2005)Google Scholar
  9. 9.
    Fisher, D.: Multi-process architecture (July 2008), http://dev.chromium.org/developers/design-documents/multi-process-architecture
  10. 10.
    Franklin, J., Paxson, V., Perrig, A., Savage, S.: An inquiry into the nature and causes of the wealth of internet miscreants. In: Proceedings of the 2007 ACM Conference on Computer and Communications Security, pp. 375–388. ACM, New York (2007)Google Scholar
  11. 11.
    Freund, Y., Schapire, R.: A short introduction to boosting. Journal of the Japanese Society for Artificial Intelligence 14(5), 771–780 (1999)Google Scholar
  12. 12.
    Freund, Y., Schapire, R.E.: Adaptive game playing using multiplicative weights. Games and Economic Behavior 29, 79–103 (1999)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Friedberg, J.: Internet fraud battlefield (April 2007), http://www.ftc.gov/bcp/workshops/proofpositive/Battlefield_Overview.pdf
  14. 14.
    Fultz, N., Grossklags, J. (eds.): Blue versus Red: Towards a model of distributed security attacks. Proceedings of the Thirteenth International Conference Financial Cryptography and Data Security (February 2009)Google Scholar
  15. 15.
    Gordon, L.A., Loeb, M.P.: The economics of information security investment. ACM Transactions on Information and System Security 5(4), 438–457 (2002)CrossRefGoogle Scholar
  16. 16.
    Grossklags, J., Christin, N., Chuang, J.: Secure or insure?: A game-theoretic analysis of information security games. In: Proceeding of the 17th International Conference on World Wide Web, pp. 209–218. ACM, New York (2008)CrossRefGoogle Scholar
  17. 17.
    Hausken, K.: Returns to information security investment: The effect of alternative information security breach functions on optimal investment and sensitivity to vulnerability. Information Systems Frontiers 8(5), 338–349 (2006)CrossRefGoogle Scholar
  18. 18.
    Herbster, M., Warmuth, M.K.: Tracking the best expert. Machine Learning 32(2), 151–178 (1998)MATHCrossRefGoogle Scholar
  19. 19.
    Howard, M.: Attack surface: Mitigate security risks by minimizing the code you expose to untrusted users. MSDN Magazine (November 2004), http://msdn.microsoft.com/en-us/magazine/cc163882.aspx
  20. 20.
    Kanich, C., Kreibich, C., Levchenko, K., Enright, B., Voelker, G.M., Paxson, V., Savage, S.: Spamalytics: An empirical analysis of spam marketing conversion. In: Proceedings of the 2008 ACM Conference on Computer and Communications Security, pp. 3–14. ACM, New York (2008)CrossRefGoogle Scholar
  21. 21.
    Kark, K., Penn, J., Dill, A.: 2008 CISO priorities: The right objectives but the wrong focus. Le Magazine de la Sécurité Informatique (April 2009)Google Scholar
  22. 22.
    Kumar, V., Telang, R., Mukhopadhyay, T.: Optimal information security architecture for the enterprise, http://ssrn.com/abstract=1086690
  23. 23.
    Lye, K.W., Wing, J.M.: Game strategies in network security. In: Proceedings of the Foundations of Computer Security Workshop, pp. 13–22 (2002)Google Scholar
  24. 24.
    Miura-Ko, R.A., Yolken, B., Mitchell, J., Bambos, N.: Security decision-making among interdependent organizations. In: Proceedings of the 21st IEEE Computer Security Foundations Symposium, pp. 66–80. IEEE Computer Society, Washington (2008)Google Scholar
  25. 25.
    Miura-Ko, R., Bambos, N.: SecureRank: A risk-based vulnerability management scheme for computing infrastructures. In: Proceedings of IEEE International Conference on Communications, pp. 1455–1460 (June 2007)Google Scholar
  26. 26.
    Ordentlich, E., Cover, T.M.: The cost of achieving the best portfolio in hindsight. Mathematics of Operations Research 23(4), 960–982 (1998)MATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Ou, X., Boyer, W.F., McQueen, M.A.: A scalable approach to attack graph generation. In: Proceedings of the 13th ACM Conference on Computer and Communications Security, pp. 336–345 (2006)Google Scholar
  28. 28.
    Pironti, J.P.: Key elements of an information security program. Information Systems Control Journal 1 (2005)Google Scholar
  29. 29.
    Rescorla, E.: Is finding security holes a good idea? IEEE Security and Privacy 3(1), 14–19 (2005)CrossRefGoogle Scholar
  30. 30.
    Varian, H.: System reliability and free riding (2001)Google Scholar
  31. 31.
    Varian, H.R.: Managing online security risks, June 1. New York Times (2000)Google Scholar
  32. 32.
    Warner, B.: Home PCs rented out in sabotage-for-hire racket. Reuters (July 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Adam Barth
    • 1
  • Benjamin I. P. Rubinstein
    • 1
  • Mukund Sundararajan
    • 3
  • John C. Mitchell
    • 4
  • Dawn Song
    • 1
  • Peter L. Bartlett
    • 1
    • 2
  1. 1.Computer Science Division 
  2. 2.Department of StatisticsUC Berkeley 
  3. 3.Google Inc.Mountain ViewCA
  4. 4.Department of Computer ScienceStanford University 

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