TY - JOUR
T1 - Moving Target Defense Meets Artificial-Intelligence-Driven Network
T2 - A Comprehensive Survey
AU - Zhang, Tao
AU - Kong, Fanyu
AU - Deng, Dongshang
AU - Tang, Xiangyun
AU - Wu, Xuangou
AU - Xu, Changqiao
AU - Zhu, Liehuang
AU - Liu, Jiqiang
AU - Ai, Bo
AU - Han, Zhu
AU - Deng, Robert H.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2025
Y1 - 2025
N2 - Based on emerging artificial intelligence (AI) tasks, cloud-edge–terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats, such as malicious network reconnaissance, side-channel attacks, and distributed denial of service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of moving target defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this article, we first provide a thorough analysis of the threats in the cloud-edge–terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this article is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.
AB - Based on emerging artificial intelligence (AI) tasks, cloud-edge–terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats, such as malicious network reconnaissance, side-channel attacks, and distributed denial of service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of moving target defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this article, we first provide a thorough analysis of the threats in the cloud-edge–terminal network. Then, we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this article is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.
KW - Artificial intelligence (AI)-driven network
KW - cloud-edge–terminal network
KW - generative AI
KW - moving target defense
KW - security analysis
UR - https://www.scopus.com/pages/publications/85216662272
U2 - 10.1109/JIOT.2025.3533016
DO - 10.1109/JIOT.2025.3533016
M3 - Article
AN - SCOPUS:85216662272
SN - 2327-4662
VL - 12
SP - 13384
EP - 13397
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 10
ER -