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By:Anthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J.D. Tygar
Published on 2018-04-30 by Cambridge University Press

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.

This Book was ranked at 8 by Google Books for keyword Machine Learning.

Book ID of Adversarial Machine Learning's Books is XkSntAEACAAJ, Book which was written byAnthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J.D. Tygarhave ETAG "95r1mo2HJ0k"

Book which was published by Cambridge University Press since 2018-04-30 have ISBNs, ISBN 13 Code is 9781107043466 and ISBN 10 Code is 1107043468

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Adversarial Machine Learning -Anthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J.D. Tygar- Google Books

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Adversarial Machine Learning byAnthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J.D. Tygar

Adversarial Machine Learning -Anthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J.D. Tygar- 9781107043466

Adversarial Machine Learning -Anthony D. Joseph,Blaine Nelson,Benjamin I. P. Rubinstein,J.D. Tygar- 1107043468

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