In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning - the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.

In  Proceedings of the 4th ACM Workshop on Artificial Intelligence and Security

Publisher  ACM

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