Adversary-Aware Learning Techniques and Trends in Cybersecurity (Paperback, 1st ed. 2021)

This book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	2022
First published:	2021
Editors:	Prithviraj Dasgupta • Joseph B. Collins • Ranjeev Mittu
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	227
Edition:	1st ed. 2021
ISBN-13:	978-3-03-055694-5
Categories:	Books > Computing & IT > Computer communications & networking > Network security Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-055694-8
Barcode:	9783030556945

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