Network Security Empowered By Artificial Intelligence
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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) 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.
Redefining Security With Cyber AI
In the rapidly evolving digital landscape, the importance of cybersecurity has never been more critical. With the increasing sophistication of cyber threats, traditional security measures often fall short in providing adequate protection. Cyber artificial intelligence (AI) offers advanced capabilities to detect, prevent, and respond to attacks in real time. As cyber threats continue to grow in complexity and frequency, the integration of AI into cybersecurity frameworks is not just advantageous but essential for maintaining robust and resilient defenses. Redefining Security With Cyber AI delves into the profound transformation of security paradigms brought about by the advent of AI. This book explores the intricate dance between the ever-expanding frontiers of digital technology and the AI-driven mechanisms that aim to safeguard them. Covering topics such as artificial neural networks, intrusion detection, and large language models, this book is an excellent resource for cybersecurity professionals, AI and machine learning researchers, IT executives and managers, policy makers and regulators, postgraduate students and educators, academicians, and more.
Cyber Resilience System Engineering Empowered by Endogenous Security and Safety
This book reveals the essence of endogenous or internal contradictions in cyberspace security issues, systematically expounds the principle of cyberspace endogenous security and safety, introduces the author-invented dynamic heterogeneous redundant (DHR) architecture with endogenous security and safety features, and theoretically answers why DHR endogenous security and safety architecture can enable network resilience engineering; the enabling role of DHR architecture solves the problem that network resilience cannot cope with unknown damage, lacks structural gain, and cannot quantify design measures. This book analyses the systematic security gains that DHR architecture enabling network resilience engineering can bring in the four purpose dimensions of prevention, defense, recovery and adaptation; gives an application example of DHR endogenous security and safety architecture enabling network resilience engineering; introduces the research and exploration of endogenous security and safety theory in wireless communication security, artificial intelligence security and other derivative application fields; and uses rich application examples. It shows that the endogenous security and safety architecture enabling network resilience engineering not only is very necessary but also has universal application significance. This book is suitable for postgraduate teaching materials or reference books of related disciplines, such as cybersecurity, network resilience engineering, confidential computing/trusted computing, information physical systems/industrial control, etc.