Adversarial Machine Learning

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Anthony D. Joseph, Blaine Nelson, Benjamin I. P. Rubinstein, J. D. TygarCambridge University Press, 2/21/2019EAN 9781107043466, ISBN10: 1107043468Hardcover, 338 pages, 25.4 x 17.8 x 1.9 cmLanguage: EnglishWritten 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.Part I. Overview of Adversarial Machine Learning1. Introduction2. Background and notation3. A framework for secure learningPart II. Causative Attacks on Machine Learning4. Attacking a hypersphere learner5. Availability attack case studySpamBayes6. Integrity attack case studyPCA detectorPart III. Exploratory Attacks on Machine Learning7. Privacy-preserving mechanisms for SVM learning8. Near-optimal evasion of classifiersPart IV. Future Directions in Adversarial Machine Learning9. Adversarial machine learning challenges. Show more