Inbunden, Engelska, 2025
1 725 kr
Skickas inom 5-8 vardagar
Xiaogang Wang – författare
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8 produkter
8 produkter
- Inbunden, Engelska, 2020
1 707 kr
Skickas inom 5-8 vardagar
- Inbunden, Engelska, 2021
1 712 kr
Skickas inom 5-8 vardagar
- Del 107 - Advances in Information Security
Network Security Empowered by Artificial Intelligence
Inbunden, Engelska, 20242 494 kr
Skickas inom 5-8 vardagar
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. - E-bokEngelska, 2024
3 242 kr
Läs direkt efter köp
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.
- Del 107 - Advances in Information Security
Network Security Empowered by Artificial Intelligence
Häftad, Engelska, 20252 494 kr
Skickas inom 10-15 vardagar
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. - Häftad, Engelska, 2024
1 324 kr
Skickas inom 10-15 vardagar
This book offers a systematic view of data services in heterogeneous wireless networks, from the perspectives of cause, governing rules, and impact of data’s mobility on such networks. Specifically, it covers application requirements break-down, network modeling, performance analysis and evaluation by examining mobile data dynamics that are particularly important to data service provisioning. Additionally, application prospects such as information dissemination, fog computing, Internet-of-Things and dynamic spectrum access are discussed on the basis of these dynamics. Theoretic analysis, example illustrations, and algorithms are also presented to provide a concise coverage of this important area of networking.Mobile data dynamics refers to the stochastic processes of information, geographical coverage and spectrum, which accompanies the movements of data across wireless networks. Owing to the challenge raised by a high level of network heterogeneity, and the innate requirement on scalability, knowledge on the evolution of mobile data dynamics is essential to the design and deployment of emerging data services and applications, such as Internet-of-Things, data/task offloading and edge-based machine learning/inference.This book is designed for researchers and advanced-level students in the field of wireless networking and edge computing, who seek to understand the models and evolutions of mobile data dynamics for future edge applications. Practitioners, who specialize in the design, operation and maintenance of edge computing systems will also want to purchase this book as a reference. - E-bokEngelska, 2024
631 kr
Läs direkt efter köp
This book offers a systematic view of data services in heterogeneous wireless networks, from the perspectives of cause, governing rules, and impact of data’s mobility on such networks. Specifically, it covers application requirements break-down, network modeling, performance analysis and evaluation by examining mobile data dynamics that are particularly important to data service provisioning. Additionally, application prospects such as information dissemination, fog computing, Internet-of-Things and dynamic spectrum access are discussed on the basis of these dynamics. Theoretic analysis, example illustrations, and algorithms are also presented to provide a concise coverage of this important area of networking.
Mobile data dynamics refers to the stochastic processes of information, geographical coverage and spectrum, which accompanies the movements of data across wireless networks. Owing to the challenge raised by a high level of network heterogeneity, and the innate requirement on scalability, knowledge on the evolution of mobile data dynamics is essential to the design and deployment of emerging data services and applications, such as Internet-of-Things, data/task offloading and edge-based machine learning/inference.
This book is designed for researchers and advanced-level students in the field of wireless networking and edge computing, who seek to understand the models and evolutions of mobile data dynamics for future edge applications. Practitioners, who specialize in the design, operation and maintenance of edge computing systems will also want to purchase this book as a reference.
