Adversarial Machine Learning
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Adversarial Machine Learning

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Anthony D. Joseph
834 g
254x177x25 mm

Joseph, Anthony D.Anthony D. Joseph is a Chancellor's Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He was formerly the Director of Intel Labs Berkeley.

Nelson, Blaine
Blaine Nelson is a Software Engineer in the Software Engineer in the Counter-Abuse Technologies (CAT) team at Google. He has previously worked at the University of Potsdam and the University of Tübingen.

Rubinstein, Benjamin I. P.
Benjamin I. P. Rubinstein is a Senior Lecturer in Computing and Information Systems at the University of Melbourne. He has previously worked at Microsoft Research, Google Research, Yahoo! Research, Intel Labs Berkeley, and IBM Research.

Tygar, J. D.
J. D. Tygar is a Professor of Computer Science and a Professor of Information Management at the University of California, Berkeley.
This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.
Part I. Overview of Adversarial Machine Learning: 1. Introduction; 2. Background and notation; 3. A framework for secure learning; Part II. Causative Attacks on Machine Learning: 4. Attacking a hypersphere learner; 5. Availability attack case study: SpamBayes; 6. Integrity attack case study: PCA detector; Part III. Exploratory Attacks on Machine Learning: 7. Privacy-preserving mechanisms for SVM learning; 8. Near-optimal evasion of classifiers; Part IV. Future Directions in Adversarial Machine Learning: 9. Adversarial machine learning challenges.
Combining essential theory and practical techniques for analysing system security, and building robust machine learning in adversarial environments, as well as including case studies on email spam and network security, this complete introduction is an invaluable resource for researchers, practitioners and students in computer security and machine learning.

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