Adversarial Machine Learning

Authors:
J. D. Tygar
Affiliations:
University of California, Berkeley
Venue:
IEEE Internet Computing
Year:
2011

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Abstract

The author briefly introduces the emerging field of adversarial machine learning, in which opponents can cause traditional machine learning algorithms to behave poorly in security applications. He gives a high-level overview and mentions several types of attacks, as well as several types of defenses, and theoretical limits derived from a study of near-optimal evasion.