ISBN: 9789819506149 - moving target defense based on artificial intelligence (springerbriefs in computer science) (13 resultados)

Condición: As New. Unread book in perfect condition.

Condición: New.

Paperback. Condición: new. Paperback. 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. 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 3 proposes a collaborative mutation-based MTD based on hierarchical reinforcement learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New.

Paperback. Condición: Brand New. 121 pages. 9.26x6.11x9.25 inches. In Stock.

Taschenbuch. Condición: Neu. Moving Target Defense Based on Artificial Intelligence | Tao Zhang (u. a.) | Taschenbuch | xi | Englisch | 2025 | Springer | EAN 9789819506149 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condición: new. Questo è un articolo print on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 124 pp. Englisch.…

Condición: New. Print on Demand.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Condición: New. PRINT ON DEMAND.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 124 pp. Englisch.…