Financial Cryptography and Data Security

Volume 6052 of the series Lecture Notes in Computer Science pp 192-206

A Learning-Based Approach to Reactive Security

  • Adam BarthAffiliated withComputer Science Division
  • , Benjamin I. P. RubinsteinAffiliated withComputer Science Division
  • , Mukund SundararajanAffiliated withGoogle Inc.
  • , John C. MitchellAffiliated withDepartment of Computer Science, Stanford University
  • , Dawn SongAffiliated withComputer Science Division
  • , Peter L. BartlettAffiliated withComputer Science DivisionDepartment of Statistics, UC Berkeley

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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.