Moving Target Defense Based on Artificial Intelligence - Zhang, Tao; Tang, Xiangyun; Kang, Jiawen; Xu, Changqiao
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Moving Target Defense (MTD) has been proposed as an innovative defense framework, which aims to introduce the dynamics, diversity and randomization into static network by the shuffling, heterogeneity and redundancy. It is born to solve the problem that traditional security solutions respond and defend against security threats after attacks occurrence, which will lead to the defender always having disadvantages in attack-defense confrontation. This book explores the challenges and solutions related to moving target defense in the cloud-edge-terminal networks. This book fills this gap by…mehr

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Produktbeschreibung
Moving Target Defense (MTD) has been proposed as an innovative defense framework, which aims to introduce the dynamics, diversity and randomization into static network by the shuffling, heterogeneity and redundancy. It is born to solve the problem that traditional security solutions respond and defend against security threats after attacks occurrence, which will lead to the defender always having disadvantages in attack-defense confrontation. This book explores the challenges and solutions related to moving target defense in the cloud-edge-terminal networks. This book fills this gap by providing a comprehensive and detailed approach to designing intelligent MTD frameworks for cloud-edge-terminal networks. It is essential reading for researchers and professionals in network security and artificial intelligence who seek innovative defense solutions. The book is organized into 6 chapters, each addressing a key area of MTD and its integration with Artificial Intelligence. Chapter 1 introduces the fundamental concepts of MTD, security challenges in cloud-edge-terminal networks, and the role of artificial intelligence in enhancing MTD. Chapter 2 delves into host address mutation based on advantage actor-critic approach. Chapter 3 proposes a collaborative mutation-based MTD based on hierarchical reinforcement learning. Chapter 4 presents roadside units configuration mutation based on proximal policy optimization approach. Chapter 5 explores route mutation based on multiagent reinforcement learning. Chapter 6 provides a summary of insights and lessons learned throughout the book and outlines future research directions in MTD.
Autorenporträt
Tao Zhang is currently an Associate Professor with the School of Cyberspace Science and Technology, Beijing Jiaotong University.. His publications include ESI highly cited paper and well-archived international journals and proceedings, such as IEEE COMST, JSAC, TIFS, TDSC, TMC, TSC, TITS, TCCN and TII etc. His research interests include network security, moving target defense, and federated learning. He serves as the associate editor of IEEE Data Descriptions, Ad Hoc Networks, EURASIP Journal on Information Security, the guest editor of Remote Sensing, Electronics and Chinese Journal of Network and Information Security, and the TPC chair and a PC member for some international conferences and workshops. He was a recipient of the Best Paper Award from NaNA 2018, IWCMC 2021, DIONE 2024, and ICA3PP 2024, and a recipient of Outstanding Paper Award from iThings 2023, and SmartCity 2024. His Ph.D. thesis was awarded the Outstanding Doctoral Dissertation by BUPT in 2023. Xiangyun Tang an Associate Professor with the School of Information Engineering, Minzu University of China. She has served as the guest editor for multiple Journals, and the TPC chair and the PC member for international conferences and workshops. She was a recipient of the Best Paper Award from IEEE ICBCTIS 2023, and a recipient of Outstanding Paper Award from IEEE iThings 2023. Her research interests include Secure Multi-party Computation and Machine Learning Security.   Jiawen Kang is a Full Professor at Guangdong University of Technology. His research interests mainly focus on AIGC, blockchain, metaverse, edge intelligence, etc. He has published more than 150 research papers in leading journals and flagship conferences. He is the co-inventor of 16 granted patents and has won IEEE VTS Best Paper Award, IEEE Communications Society CSIM Technical Committee Best Journal Paper Award, IEEE Best Land Transportation Paper Award, IEEE HITC Award for Excellence in Hyper-Intelligence Systems (Early Career Researcher award), IEEE Computer Society Smart Computing Special Technical Community Early-Career Award, and 13 best paper awards of international conferences as well. He is an IEEE Senior Member and is listed in the World’s Top 2% of Scientists identified by Stanford University. He is now serving as the editor or guest editor for 11 leading journals. He received IEEE Outstanding Leadership Award for 2022 EUC Conference (Program Chair) and IEEE Distinguished Services Award for 16th IEEE iThings Conference (Program Chair). He is a vice-chair of IEEE Technical Committee on Cognitive Networks Special Interest Group on "Wireless Blockchain Networks. Changqiao Xu is a Professor with the State Key Laboratory of Networking and Switching Technology, and Director of the Network Architecture Research Center at BUPT. His research interests include Future Internet Technology, Mobile Networking, Multimedia Communications, and Network Security. He has edited two books and published over 200 technical papers in prestigious international journals and conferences, including IEEE Comm. Magazine, IEEE/ACM ToN, IEEE TMC, INFOCOM, ACM Multimedia etc. He has served a number of international conferences and workshops as a Co-Chair and TPC member. He is currently serving as the Editor-in-Chief of Transactions on Emerging Telecommunications Technologies.