A Learning-Based Approach to Reactive Security

doi:10.1109/TDSC.2011.42

A Learning-Based Approach to Reactive Security

2012-07-01

14

OA

AI

A

Adam Barth *

B

Benjamin I. P. Rubinstein

DOI：10.1109/TDSC.2011.42

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Abstract

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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.

Keywords:

Reactive security

risk management

attack graphs

online learning

adversarial learning

game theory

A Learning-Based Approach to Reactive Security

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