文献类型：期刊文献

英文题名：Protecting Cyber-Physical Systems via Vendor-Constrained Security Auditing with Reinforcement Learning

作者：Wang, Nan[1]; Li, Kai[1]; Lu, Lijun[1]; Zhao, Zhiwei[1]; Ma, Zhiyuan[2]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China; [2] Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai, China

年份：2025

外文期刊名：Proceedings -Design, Automation and Test in Europe, DATE

收录：EI(收录号：20252218534763)

语种：英文

外文关键词：Computer viruses - Cyber attacks - Intelligent systems - Multi agent systems

摘要：Hardware Trojans may cause security issues in cyber-physical systems (CPSs), and recently proposed mutual auditing frameworks have helped build trustworthy CPSs with untrustworthy devices by requiring neighboring devices from different vendors. However, this may cause severe multi-vendor integration challenges, such as expensive, hard-to-maintain, and insufficient vendors to purchase devices. In this work, we improve the mutual auditing framework by maintaining the security of the CPSs with fewer vendors. First, the vendor-constrained security auditing framework is introduced to enhance the security of the CPS network with limited vendors, where side auditing detects the hardware Trojan collusion between neighboring nodes and infected node isolation stops the spread of active HTs. Second, a multi-agent cooperative reinforcement learning-based method is proposed to assign devices with proper vendors in the context of security auditing, and it provides solutions with a minimized number of offline nodes due to the HT infection. The experimental results show that our proposed method reduces the number of vendors needed by 40.95%, and only causes an increment of 0.39% infected nodes. ? 2025 EDAA.