Electromagnetic Side-Channel Hardware Trojan Detection Based on Transfer Learning

Abstract

Hardware Trojan detection method has been given particular attentions by hardware security researchers since the failure of Syrian radars in 2007. Electromagnetic side-channel analysis is a promising method which is widely used in practice due to its advantage of efficiency, non-touch and high accuracy. In this brief, we propose a novel method using electromagnetic side-channel signal for hardware Trojan detection by transfer learning. Firstly, time-frequency information of electromagnetic signal is extracted by continuous wavelet transform to take full advantage of useful information in time domain and frequency domain. Then, time-frequency information is fed to transfer learning network to get the classification result. In order to further improve the result, we use transfer learning to further extract the key features in time-frequency information. Finally, the key features are classified by the support vector machine to improve the accuracy. The experiment is conducted on a stand FPGA board and advanced encryption standard circuit is used as the benchmark circuit. Experimental results show that our methods can improve the result efficiently.