LEADER 04350nam 22006615 450 001 9911046543403321 005 20251001130541.0 010 $a981-9506-15-8 024 7 $a10.1007/978-981-95-0615-6 035 $a(CKB)41521071100041 035 $a(MiAaPQ)EBC32323277 035 $a(Au-PeEL)EBL32323277 035 $a(DE-He213)978-981-95-0615-6 035 $a(OCoLC)1547928231 035 $a(EXLCZ)9941521071100041 100 $a20251001d2026 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMoving Target Defense Based on Artificial Intelligence /$fby Tao Zhang, Xiangyun Tang, Jiawen Kang, Changqiao Xu 205 $a1st ed. 2026. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2026. 215 $a1 online resource (164 pages) 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$a981-9506-14-X 327 $aChapter 1 Introduction of Moving Target Defense -- Chapter 2 Host Address Mutation based on Advantage Actor-Critic Approach -- Chapter 3 Service Function Chain Migration based on Proximal Policy Optimization Approach -- Chapter 4 Collaborative Mutation-based Moving Target Defense based on Hierarchical Reinforcement Learning -- Chapter 5 Roadside Units Configuration Mutation based on Proximal Policy Optimization Approach -- Chapter 6 Route Mutation based on Multiagent Reinforcement Learning -- Chapter 7: Secure and Trusted Collaborative Learning based on Blockchain -- Chapter 8 Summary and Future Research Directions. 330 $aMoving Target Defense (MTD) has been proposed as an innovative defense framework, which aims to introduce the dynamics, diversity and randomization into static network by the shuffling, heterogeneity and redundancy. It is born to solve the problem that traditional security solutions respond and defend against security threats after attacks occurrence, which will lead to the defender always having disadvantages in attack-defense confrontation. This book explores the challenges and solutions related to moving target defense in the cloud-edge-terminal networks. This book fills this gap by providing a comprehensive and detailed approach to designing intelligent MTD frameworks for cloud-edge-terminal networks. It is essential reading for researchers and professionals in network security and artificial intelligence who seek innovative defense solutions. The book is organized into 6 chapters, each addressing a key area of MTD and its integration with Artificial Intelligence. Chapter 1 introduces the fundamental concepts of MTD, security challenges in cloud-edge-terminal networks, and the role of artificial intelligence in enhancing MTD. Chapter 2 delves into host address mutation based on advantage actor-critic approach. Chapter 3 proposes a collaborative mutation-based MTD based on hierarchical reinforcement learning. Chapter 4 presents roadside units configuration mutation based on proximal policy optimization approach. Chapter 5 explores route mutation based on multiagent reinforcement learning. Chapter 6 provides a summary of insights and lessons learned throughout the book and outlines future research directions in MTD. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aComputer networks$xSecurity measures 606 $aData protection 606 $aComputer networks 606 $aCloud computing 606 $aMobile and Network Security 606 $aSecurity Services 606 $aComputer Communication Networks 606 $aCloud Computing 615 0$aComputer networks$xSecurity measures. 615 0$aData protection. 615 0$aComputer networks. 615 0$aCloud computing. 615 14$aMobile and Network Security. 615 24$aSecurity Services. 615 24$aComputer Communication Networks. 615 24$aCloud Computing. 676 $a005.8 700 $aZhang$b Tao$01272789 701 $aTang$b Xiangyun$01769175 701 $aKang$b Jiawen$01862484 701 $aXu$b Changqiao$01862485 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911046543403321 996 $aMoving Target Defense Based on Artificial Intelligence$94468766 997 $aUNINA