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Network Security Empowered by Artificial Intelligence

This book introduces cutting-edge methods on security in spectrum management, mobile networks and next-generation wireless networks in the era of artificial intelligence (AI) and machine learning (ML). This book includes four parts: (a) Architecture Innovations and Security in 5G Networks, (b) Secur...

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Korporativní autor: SpringerLink (Online service)
Další autoři: Chen, Yingying (Editor), Wu, Jie (Editor), Yu, Paul (Editor), Wang, Xiaogang (Editor)
Médium: Elektronický zdroj E-kniha
Jazyk:angličtina
Vydáno: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Vydání:1st ed. 2024.
Edice:Advances in Information Security, 107
Témata:
Artificial intelligence.
Computer networks > Security measures.
Machine learning.
Artificial Intelligence.
Mobile and Network Security.
Machine Learning.
On-line přístup:https://doi.org/10.1007/978-3-031-53510-9
Tagy: Přidat tag
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Obsah:
  • Preface
  • Part I. Architecture Innovations and Security in 5G Networks
  • Chapter. 1. nCore: Clean Slate Next-G Mobile Core Network Architecture for Scalability and Low Latency
  • Chapter. 2. Decision-Dominant Strategic Defense Against Lateral Movement for 5G Zero-Trust Multi-Domain Networks
  • Part. II. Security in Artificial Intelligence-enabled Intrusion Detection Systems
  • Chapter. 3. Artificial Intelligence and Machine Learning for Network Security – Quo Vadis?
  • Chapter 4. Understanding the Ineffectiveness of the Transfer Attack in Intrusion Detection System
  • Chapter. 5. Advanced ML/DL-based Intrusion Detection Systems for Software-Defined Networks
  • Part III. Attack and Defense in Artificial Intelligence-enabled Wireless Systems
  • Chapter. 6. Deep Learning for Robust and Secure Wireless Communications
  • Chapter. 7. Universal Targeted Adversarial Attacks Against mmWave-based Human Activity Recognition
  • Chapter. 8. AdversarialMachine Learning for Wireless Localization
  • Chapter. 9. Localizing Spectrum Offenders Using Crowdsourcing
  • Chapter. 10. Adversarial Online Reinforcement Learning Under Limited Defender Resources
  • Part. IV. Security in Network-enabled Applications
  • Chapter. 11. Security and Privacy of Augmented Reality Systems
  • Chapter. 12. Securing Augmented Reality Applications
  • Chapter. 13. On the Robustness of Image-based Malware Detection against Adversarial Attacks
  • Chapter. 14. The Cost of Privacy: A Comprehensive Analysis of the Security Issues in Federated Learning
  • Chapter. 15. Lessons Learned and Future Directions for Security, Resilience and Artificial Intelligence in Cyber Physical Systems.

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