Moving Target Defense Based on Artificial Intelligence / by Tao Zhang, Xiangyun Tang, Jiawen Kang, Changqiao Xu.

Format:

Book

Author/Creator:

Zhang, Tao.

Contributor:

Tang, Xiangyun.

Kang, Jiawen.

Xu, Changqiao.

Series:

SpringerBriefs in Computer Science, 2191-5776

Language:

English

Subjects (All):

Computer networks--Security measures.

Computer networks.

Data protection.

Cloud computing.

Mobile and Network Security.

Security Services.

Computer Communication Networks.

Cloud Computing.

Local Subjects:

Mobile and Network Security.

Security Services.

Computer Communication Networks.

Cloud Computing.

Physical Description:

1 online resource (164 pages)

Edition:

1st ed. 2026.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2026.

Summary:

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.

Contents:

Chapter 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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

981-9506-15-8

9789819506156

OCLC:

1547928231