A neural network framework based on ConvNeXt for side-channel hardware Trojan detection

Abstract

Researchers in the field of hardware security have been dedicated to the study of hardware Trojan detection. Among the various approaches, side-channel detection methods are widely used because of their high detection accuracy and fewer constraints. However, most side-channel detection methods cannot make full use of side-channel information. In this paper, we propose a framework that utilizes the continuous wavelet transform to convert time-series information and employs an improved ConvNeXt network to detect hardware Trojans. This detection framework first converts one-dimensional time-series information into a two-dimensional time–frequency map using the continuous wavelet transform to leverage frequency information in electromagnetic side-channel signals. Then, the two-dimensional time–frequency map is fed into the improved ConvNeXt network, which increases the weight of the informative parts in the two-dimensional time–frequency map and enhances detection efficiency. The results indicate that the method proposed in this paper significantly improves the accuracy of hardware Trojan detection.