Adversarial Machine Learning

ISBN-13: 9781107043466 / Angielski / Twarda / 2019 / 338 str.

Anthony D. Joseph; Blaine Nelson; Benjamin I. P. Rubinstein

Opis

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.

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Recenzje

'This is a timely book. Right time and right book, written with an authoritative but inclusive style. Machine learning is becoming ubiquitous. But for people to trust it, they first need to understand how reliable it is.' Fabio Roli, University of Cagliari, Italy

Spis treści

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.