Wireless Security Enhancement Framework Based on AI and RIS in Industrial IoT Networks

Authors:
Publisher website
Google Scholar
Abstract
Reconfigurable Intelligent Surface (RIS) technology, closely integrated with Artificial Intelligence (AI) as one of the Physical Layer (PL) technology approaches, is considered an ef-fective solution for enhancing security in industrial Internet of Things (IIoT) networks. Integrating RIS, AI, and heterogeneous IIoT networks presents a promising approach to improving the se-curity of wireless communications in IIoT. This paper discusses new AI-based Physical Layer Authentication (PLA) approaches and how they can be integrated with RIS to enable identity authen-tication and malicious node detection, aiming to provide light-weight isolation in IIoT terminals. An anti-jamming and secure communication framework for IIoT is proposed, which leverages Deep Reinforcement Learning (DRL) and RIS to defend against malicious jamming and eavesdropping targeting legitimate users' channels. Additionally, a foundational framework that employs Quantum Reinforcement Learning (QRL) is presented to improve RIS-assisted secure communication. Furthermore, the threats posed by Generative AI (GAI) to RIS-assisted IIoT communica-tion networks and possible defense schemes are provided. Finally, the paper concludes by discussing research opportunities and fu-ture challenges.
Funding Information
  • National Natural Science Foundation of China (62201132, U23B2021)

This publication has 14 references indexed in Scilit: