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September/October 2011 (vol. 15 no. 5)
pp. 4-6
J.D. Tygar, University of California, Berkeley

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.

Index Terms:
machine learning, adversarial machine learning, computer security, spam email, intrusion detection
Citation:
J.D. Tygar, "Adversarial Machine Learning," IEEE Internet Computing, vol. 15, no. 5, pp. 4-6, Sept.-Oct. 2011, doi:10.1109/MIC.2011.112
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