Adversarial Machine Learning (Hardcover)

Written 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.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	February 2019
Authors:	Anthony D. Joseph • Blaine Nelson • Benjamin I. P. Rubinstein • J. D. Tygar
Dimensions:	254 x 178 x 19mm (L x W x T)
Format:	Hardcover
Pages:	338
ISBN-13:	978-1-107-04346-6
Categories:	Books > Computing & IT > Computer communications & networking > Network security Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-107-04346-8
Barcode:	9781107043466

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!