Abstract:
Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response...Show MoreMetadata
Abstract:
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 paper, we firstly 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 paper is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network.
Published in: IEEE Internet of Things Journal ( Early Access )
Funding Agency:
School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing, China
Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet, Anhui, China
School of Information Engineering, Minzu University of China, Beijing, China
College of Computer Science, Chongqing University, Chongqing, China
School of Information Engineering, Minzu University of China, Beijing, China
Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry, School of Computer Science and Technology, Anhui University of Technology, Anhui, China
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China
School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing, China
School of Electronic and Information Engineering and Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing, China
Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA
Department of Information Systems, Singapore Management University, Singapore, Singapore
School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing, China
Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet, Anhui, China
School of Information Engineering, Minzu University of China, Beijing, China
College of Computer Science, Chongqing University, Chongqing, China
School of Information Engineering, Minzu University of China, Beijing, China
Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry, School of Computer Science and Technology, Anhui University of Technology, Anhui, China
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China
School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing, China
School of Electronic and Information Engineering and Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing, China
Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA
Department of Information Systems, Singapore Management University, Singapore, Singapore
