AI, machine learning and deep learning a security perspective
Part I. Secure AI/ML Systems: Attack Models1. Machine Learning Attack Models, 2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications, 3. Threat of Adversarial Attacks to Deep Learning: A Survey, 4. Attack Models for Collaborative Deep Learning, 5. Attacks...
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| Weitere Verfasser: | , |
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Boca Raton, London, New York
CRS Press
2023
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| Ausgabe: | First edition |
| Schlagworte: |
Algorithmen und Datenstrukturen
> Algorithms & data structures
> Artificial intelligence
> Automatic control engineering
> COMPUTERS / Artificial Intelligence
> COMPUTERS / Programming / Algorithms
> COMPUTERS / Security / General
> COMPUTERS / Social Aspects / General
> Computer security
> Computersicherheit
> Digital- und Informationstechnologien: Rechtliche Aspekte
> Künstliche Intelligenz
> LAW / Computer & Internet
> Legal aspects of IT
> Regelungstechnik
> TECHNOLOGY & ENGINEERING / Automation
> Maschinelles Lernen
> Deep learning
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| Zusammenfassung: | Part I. Secure AI/ML Systems: Attack Models1. Machine Learning Attack Models, 2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications, 3. Threat of Adversarial Attacks to Deep Learning: A Survey, 4. Attack Models for Collaborative Deep Learning, 5. Attacks on Deep Reinforcement Learning Systems: A Tutorial, 6. Trust and Security of Deep Reinforcement Learning, 7. IoT Threat Modeling using Bayesian NetworksPart II. Secure AI/ML Systems: Defenses8. Survey of Machine Learning Defense Strategies, 9. Defenses Against Deep Learning Attacks, 10. Defensive Schemes for Cyber Security of Deep Reinforcement Learning, 11. Adversarial Attacks on Machine Learning Models in Cyber-Physical Systems, 12. Federated Learning and Blockchain: An Opportunity for Artificial Intelligence with Data RegulationPart III. Using AI/ML Algorithms for Cyber Security13. Using Machine Learning for Cyber Security: Overview, 14. Performance of Machine Learning and Big Data Analytics Paradigms in Cyber Security, 15. Using ML and DL Algorithms for Intrusion Detection in Industrial Internet of Things. Part IV. Applications16. On Detecting Interest Flooding Attacks in Named Data Networking (NDN)-based IoT Searches, 17. Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks, 18. An Artificial Intelligence-assisted Security Analysis of Smart Healthcare Systems, 19. A User-centric Focus for Detecting Phishing Emails Today AI and Machine/Deep Learning have become the hottest areas in the information technology. This book aims to provide a complete picture on the challenges and solutions to the security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xii, 333 Seiten Illustrationen, Diagramme |
| ISBN: | 9781032034041 978-1-032-03404-1 9781032034058 978-1-032-03405-8 |