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Adversary-Aware Learning Techniques and Trends in Cybersecurity. 2021 ed.

Adversary-Aware Learning Techniques and Trends in Cybersecurity. 2021 ed.

・ISBN 978-3-030-55694-5 paper EUR 139.99

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お気に入り
著者・編者Dasgupta, Prithviraj / Collins, Joseph B. / Mittu, Ranjeev (eds.),
出版社 (Springer Nature Switzerland AG, SZ)
出版年月2022
ページ数227 pp.
言語ENG
ニュース番号<A03-73279>

解説

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.