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.

An Introduction to the Machine Learning Empowered Intelligent Data Center Networking Fundamentals of Machine Learning in Data Center Networks. This book reviews the common learning paradigms that are widely used in data centernetworks, and offers an introduction to data collection and data processing in data centers. Additionally, it proposes a multi-dimensional and multi-perspective solution quality assessment system called REBEL-3S. The book offers readers a solid foundation for conducting research in the field of AI-assisted data center networks. Comprehensive Survey of AI-assisted Intelligent Data Center Networks. This book comprehensively investigates the peer-reviewed literature published in recent years. The wide range of machine learning techniques is fully reflected to allow fair comparisons. In addition, the book provides in-depth analysis and enlightening discussions on the effectiveness of AI in DCNs from various perspectives, covering flow prediction, flow classification, load balancing, resource management, energy management, routing optimization, congestion control, fault management, and network security. Provides a Broad Overview with Key Insights. This book introduces several novel intelligent networking concepts pioneered by real-world industries, such as Knowledge Defined Networks, Self-Driving Networks, Intent-driven Networks and Intent-based Networks. Moreover, it shares unique insights into the technological evolution of the fusion of artificial intelligence and data center networks, together with selected challenges and future research opportunities.

This book highlights the applications and theory of artificial intelligence in the domain of cybersecurity. The book proposes new approaches and ideas to present applications of innovative approaches in real-time environments. In the past few decades, there has been an exponential rise in the application of artificial intelligence technologies (such as deep learning, machine learning, blockchain) for solving complex and intricate problems arising in the domain of cybersecurity. The versatility of these techniques has made them a favorite among scientists and researchers working in diverse areas. This book serves as a reference for young scholars, researchers, and industry professionals working in the field of Artificial Intelligence and Cybersecurity.

Conquer Cybersecurity Challenges with AI: Your Ultimate Guide In today's ever-connected world, cyber threats loom large. Traditional security methods struggle to keep pace with the sophistication and speed of modern attacks. But there's a new weapon in the fight for digital defense: Artificial Intelligence (AI). This comprehensive guide explores how AI is revolutionizing cybersecurity, empowering businesses and individuals to build a more secure future. Demystifying AI for Cybersecurity: Unveiling the magic behind the curtain, this guide dives into the world of AI and machine learning (ML). We'll explain how AI algorithms analyze vast amounts of data, identify patterns, and predict potential attacks, acting as a vigilant guardian on your digital watchtower. AI's Arsenal of Defensive Tools: From proactive threat detection and prevention to swift incident response and forensics, discover how AI empowers your security team. Explore how AI can identify vulnerabilities before attackers exploit them, analyze network traffic for suspicious activity, and automate the analysis of security alerts, saving valuable time and resources. AI Outmaneuvering Phishing Attacks: Phishing scams remain a persistent threat. This guide unveils how AI thwarts these attempts with deception and advanced detection. Learn about honeypots that lure attackers away from real systems, and AI-powered simulations that train employees to identify phishing tactics. Discover how AI analyzes email content and user behavior to flag suspicious attempts before they cause harm. Understanding User Behavior with UEBA: Imagine a guardian angel monitoring your network for unusual activity. UEBA (User and Entity Behavior Analytics) is just that. This guide explores how AI analyzes user and entity behavior to identify potential insider threats or compromised accounts. Learn how UEBA establishes baselines for normal activity and flags anomalies that might indicate a security breach. Securing the Cloud with AI's Power: The cloud offers flexibility and scalability, but security concerns remain. This guide delves into how AI safeguards cloud-based infrastructure and data. Discover how AI continuously monitors cloud workloads, detects threats in real-time, and automates certain security responses, minimizing the impact of incidents. Beyond the Technology: The Human Element While AI offers immense potential, human expertise remains irreplaceable. This guide emphasizes the importance of collaboration between humans and AI. Explore how security professionals leverage AI insights to make critical decisions and ensure ethical considerations are addressed throughout the cybersecurity process. The Future of AI and Cybersecurity: A Collaborative Journey The cybersecurity landscape is constantly evolving, and AI is at the forefront of this transformation. This guide explores the exciting possibilities and ongoing challenges that lie ahead, including AI's ability to adapt to emerging threats and the potential for automated incident response. Embrace a Secure Future with AI: This guide empowers you to understand AI's role in cybersecurity. By leveraging this powerful technology responsibly, you can build a more robust defense against cyber threats. Take the first step towards a more secure digital future – explore the power of AI in cybersecurity today!

This contributed volume provides the state-of-the-art development on security and privacy for cyber-physical systems (CPS) and industrial Internet of Things (IIoT). More specifically, this book discusses the security challenges in CPS and IIoT systems as well as how Artificial Intelligence (AI) and Machine Learning (ML) can be used to address these challenges. Furthermore, this book proposes various defence strategies, including intelligent cyber-attack and anomaly detection algorithms for different IIoT applications. Each chapter corresponds to an important snapshot including an overview of the opportunities and challenges of realizing the AI in IIoT environments, issues related to data security, privacy and application of blockchain technology in the IIoT environment. This book also examines more advanced and specific topics in AI-based solutions developed for efficient anomaly detection in IIoT environments. Different AI/ML techniques including deep representation learning, Snapshot Ensemble Deep Neural Network (SEDNN), federated learning and multi-stage learning are discussed and analysed as well. Researchers and professionals working in computer security with an emphasis on the scientific foundations and engineering techniques for securing IIoT systems and their underlying computing and communicating systems will find this book useful as a reference. The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, cyber security, and information systems. It also applies to advanced-level students studying electrical engineering and system engineering, who would benefit from the case studies.

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users. This book reviews the latest research in the area, including effective applications of machine learning methods in cybersecurity solutions and the urgent security risks related to the machine learning models. The book is divided into three parts: Cyber Security Based on Machine Learning; Security in Machine Learning Methods and Systems; and Security and Privacy in Outsourced Machine Learning. Addressing hot topics in cybersecurity and written by leading researchers in the field, the book features self-contained chapters to allow readers to select topics that are relevant to their needs. It is a valuable resource for all those interested in cybersecurity and robust machine learning, including graduate students and academic and industrial researchers, wanting to gain insights into cutting-edge research topics, as well as related tools and inspiring innovations.