Produktnummer:
18a1b1b87cbce64602b7b3781571693481
| Themengebiete: | adversairal robots adversarial machine learning automated decision making cyber-physical systems cyber security deep learning game theory generative adversarial networks multi-agent systems security threats and vulnerabilities in machine learning |
|---|---|
| Veröffentlichungsdatum: | 23.01.2022 |
| EAN: | 9783030556945 |
| Sprache: | Englisch |
| Seitenzahl: | 227 |
| Produktart: | Kartoniert / Broschiert |
| Herausgeber: | Collins, Joseph B. Dasgupta, Prithviraj Mittu, Ranjeev |
| Verlag: | Springer International Publishing |
Produktinformationen "Adversary-Aware Learning Techniques and Trends in Cybersecurity"
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.
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