Adversary-Aware Learning Techniques and Trends in Cybersecurity

Prithviraj Dasgupta (Curatore)

Joseph B. Collins (Curatore)

Springer Nature Switzerland AG, 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.

• Curatore: Prithviraj Dasgupta, Joseph B. Collins
• Editore: Springer Nature Switzerland AG
• Anno: 2021
• Rilegatura: Hardback
• Pagine: 227 p.
• Dimensioni: 235 x 155 mm
• EAN: 9783030556914

Testo in English