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Issue No. 05 - September/October (2011 vol. 15)
ISSN: 1089-7801
pp: 4-6
J.D. Tygar , University of California, Berkeley
<p>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.</p>
machine learning, adversarial machine learning, computer security, spam email, intrusion detection
J.D. Tygar, "Adversarial Machine Learning", IEEE Internet Computing, vol. 15, no. , pp. 4-6, September/October 2011, doi:10.1109/MIC.2011.112
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