A Novel Power System Side Channel Attack Method Based on Machine Learning CNN-Transformer

Abstract

Continuous advancements in cryptography and information technology have rendered current cryptographic algorithms highly robust against traditional cryptanalysis methods. However, in modern power systems, the equipment’s inherent process characteristics result in the leakage of side channel information during the operation of cryptographic algorithms. This information includes power consumption, electromagnetic signals, and timing data. Adversaries can exploit these side channels to compromise encryption keys. To address this issue, a groundbreaking power system side-channel attack method is introduced in this paper, leveraging the CNN-Transformer architecture in machine learning. The proposed approach utilizes power consumption analysis techniques to identify relevant points of interest in the side channel power consumption data. By employing a machine learning model for training, encryption can be breached. Empirical results demonstrate the superior attack efficiency of the model compared to LSTM and CNN models in side channel attacks.