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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| Další autoři: | , , , |
| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
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Springer Nature Switzerland : Imprint: Springer,
2024.
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| Vydání: | 1st ed. 2024. |
| Edice: | Advances in Information Security,
107 |
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| On-line přístup: | https://doi.org/10.1007/978-3-031-53510-9 |
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| 245 | 1 | 0 | |a Network Security Empowered by Artificial Intelligence |h [electronic resource] / |c edited by Yingying Chen, Jie Wu, Paul Yu, Xiaogang Wang. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a XIX, 432 p. 127 illus., 112 illus. in color. |b online resource. | ||
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| 490 | 1 | |a Advances in Information Security, |x 2512-2193 ; |v 107 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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) Security in Artificial Intelligence-enabled Intrusion Detection Systems. (c) Attack and Defense in Artificial Intelligence-enabled Wireless Systems, (d) Security in Network-enabled Applications. The first part discusses the architectural innovations and security challenges of 5G networks, highlighting novel network structures and strategies to counter vulnerabilities. The second part provides a comprehensive analysis of intrusion detection systems and the pivotal role of AI and machine learning in defense and vulnerability assessment. The third part focuses on wireless systems, where deep learning is explored to enhance wireless communication security. The final part broadens the scope, examining the applications of these emerging technologies in network-enabled fields. The advancement of AI/ML has led to new opportunities for efficient tactical communication and network systems, but also new vulnerabilities. Along this direction, innovative AI-driven solutions, such as game-theoretic frameworks and zero-trust architectures are developed to strengthen defenses against sophisticated cyber threats. Adversarial training methods are adopted to augment this security further. Simultaneously, deep learning techniques are emerging as effective tools for securing wireless communications and improving intrusion detection systems. Additionally, distributed machine learning, exemplified by federated learning, is revolutionizing security model training. Moreover, the integration of AI into network security, especially in cyber-physical systems, demands careful consideration to ensure it aligns with the dynamics of these systems. This book is valuable for academics, researchers, and students in AI/ML, network security, and related fields. It serves as a resource for those in computer networks, AI, ML, and data science, and can be used as a reference or secondary textbook. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer networks |x Security measures. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 1 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Mobile and Network Security. |
| 650 | 2 | 4 | |a Machine Learning. |
| 700 | 1 | |a Chen, Yingying. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Wu, Jie. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Yu, Paul. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 700 | 1 | |a Wang, Xiaogang. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
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