文献类型：期刊文献

英文题名：Detecting Power Analysis Attacks with Machine Learning Through Voltage Differential Monitoring

作者：Wang, Nan[1]; Liu, Ruichao[1]; Xia, Weiqing[1]; Shan, Yufeng[2]; Chao, Qun[3]; Chen, Song[4]

机构：[1] East China University of Science and Technology, School of Information Science and Engineering, Shanghai, 200237, China; [2] Huawei Technologies Co., Ltd, Shanghai, 200240, China; [3] Shanghai Jiao Tong University, School of Mechanical Engineering, Shanghai, 200240, China; [4] University of Science and Technology of China, School of Microelectronics, Hefei, 230026, China

年份：2025

外文期刊名：IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

收录：EI(收录号：20254419401270)

语种：英文

外文关键词：Drops - Electric power transmission networks - Electric resistance - Hardware security - Learning systems - Network security - Side channel attack - Signal detection - Voltage measurement

摘要：Modern power analysis attacks (PAAs) pose a significant threat to hardware security, and reliably securing integrated systems against advanced PAAs has become an essential design target. The fundamental principle of PAA detection lies in identifying voltage drops induced by malicious probe insertion. However, conventional detection methods often suffer from reduced accuracy when voltage information is obscured by noise. To address this limitation, a real-time PAA detection technique is proposed to achieve high detection accuracy even in environments with significant voltage noise. The voltages of power grid nodes are initially acquired through voltage sensors, and the voltage differential between power grid nodes is evaluated by performing multiple voltage comparisons within a time period, which effectively mitigates the noise effects. Then, these differential measurements are processed by a linear support vector machine (SVM) model to identify anomalous voltage drops. To further optimize hardware efficiency, a reinforcement learning-based method is developed to determine sensor deployment, minimizing power and area overheads while maintaining detection accuracy. Experimental validation of our method demonstrates a high detection accuracy of 87.13% for 0.2 Ω resistance insertions, even under severe noise conditions (20% of Vdd). ? 1982-2012 IEEE.