Identification of Stealthy Hardware Trojans through On-Chip Temperature Sensing and an Autoencoder-Based Machine Learning Algorithm
Identification of Stealthy Hardware Trojans through On-Chip Temperature Sensing and an Autoencoder-Based Machine Learning Algorithm
复制标题
DOI：
10.1109/mwscas57524.2023.10405958
复制
发表时间：
2023-08
期刊：
影响因子：
--
通讯作者：
Thomas Gourousis;Ziyue Zhang;Mengting Yan;Milin Zhang;Ankit Mittal;A. Shrivastava;Francesco Restuccia;Yunsi Fei;Marvin Onabajo
中科院分区：
文献类型：
--
作者：
Thomas Gourousis;Ziyue Zhang;Mengting Yan;Milin Zhang;Ankit Mittal;A. Shrivastava;Francesco Restuccia;Yunsi Fei;Marvin Onabajo
本文提出了一种基于非侵入式片上温度传感的异常检测方法，用于硬件木马检测，该方法与基于自编码器的机器学习（ML）算法的异常检测技术相结合。在一个案例研究中，开发的算法识别硬件木马的准确率超过90%，木马功耗低至测试电路的2.5%。除了识别木马之外，该算法还可以提供木马在芯片上的位置信息。基于机器学习的片上异常检测方法正在开发中，作为现代电子系统，特别是物联网应用中增强硬件安全性的解决方案。
This paper presents an anomaly detection approach with non-invasive on-chip temperature sensing for hardware Trojan detection, which is coupled with a proposed anomaly detection technique using an autoencoder-based machine learning (ML) algorithm. In a case study, the developed algorithm identifies the Hardware Trojan with over 90% accuracy for a Trojan power consumption that is as low as 2.5% of the circuit under test. Besides identifying the Trojan, the algorithm can also provide information about the location of the Trojan on the chip. The proposed on-chip anomaly detection approach with machine learning is under development as a solution for enhanced hardware security in modern electronic systems, particularly for Internet of Things applications.