Adversarial Machine Learning

'Data Science practitioners tend to be unaware of how easy it is for adversaries to manipulate and misuse adaptive machine learning systems. This book demonstrates the severity of the problem by providing a taxonomy of attacks and studies of adversarial learning. It analyzes older attacks as well as recently discovered surprising weaknesses in deep learning systems. A variety of defenses are discussed for different learning systems and attack types that could help researchers and developers design systems that are more robust to attacks.' Richard Lippmann, Lincoln Laboratory, Massachusetts Institute of Technology

'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

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. 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 Tbingen. 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. J. D. Tygar is a Professor of Computer Science and a Professor of Information Management at the University of California, Berkeley.

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.