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

Abstract

AbstractResearchers 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.