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Issue No. 04 - July-Aug. (2012 vol. 9)
ISSN: 1545-5971
pp: 482-493
Adam Barth , Google Inc., Mountain View
Benjamin I.P. Rubinstein , Microsoft Research, Mountain View
Mukund Sundararajan , Google Inc., Mountain View
John C. Mitchell , Stanford University, Stanford
Dawn Song , University of California Berkeley, Berkeley
Peter L. Bartlett , University of California Berkeley, Berkeley
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.
Reactive security, risk management, attack graphs, online learning, adversarial learning, game theory.

P. L. Bartlett, B. I. Rubinstein, A. Barth, J. C. Mitchell, M. Sundararajan and D. Song, "A Learning-Based Approach to Reactive Security," in IEEE Transactions on Dependable and Secure Computing, vol. 9, no. , pp. 482-493, 2011.
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