Adversarial Machine Learning
- 14 %

Adversarial Machine Learning

| Lieferzeit:3-5 Tage I

Unser bisheriger Preis:ORGPRICE: 90,40 €

Jetzt 77,99 €*

ISBN-13:
9781107043466
Erscheinungsdatum:
01.02.2019
Seiten:
325
Autor:
Anthony D. Joseph
Gewicht:
834 g
Format:
254x177x25 mm
Sprache:
Englisch
Beschreibung:

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