Adversarial classification: An adversarial risk analysis approach
This work addresses adversarial classification for security applications, offering a novel approach to overcome limitations in current methods.
The paper tackles adversarial classification problems in security settings by proposing an alternative framework based on adversarial risk analysis, addressing the unrealistic common knowledge assumptions in existing game-theoretical approaches, with computational and implementation issues discussed.
Classification problems in security settings are usually contemplated as confrontations in which one or more adversaries try to fool a classifier to obtain a benefit. Most approaches to such adversarial classification problems have focused on game theoretical ideas with strong underlying common knowledge assumptions, which are actually not realistic in security domains. We provide an alternative framework to such problem based on adversarial risk analysis, which we illustrate with several examples. Computational and implementation issues are discussed.