Paper 2022/396

Side-channel attacks based on power trace decomposition

Fanliang Hu and Huanyu Wang and Junnian Wang

Abstract

Side Channel Attacks (SCAs), an attack that exploits the physical information generated when an encryption algorithm is executed on a device to recover the key, have become one of the key threats to the security of encrypted devices. Recently, with the development of deep learning, deep learning techniques have been applied to side channel attacks with good results on publicly available dataset experiences. In this paper, we propose a power tracking decomposition method that divides the original power tracking into two parts, where the data-influenced part is defined as data power tracking and the other part is defined as device constant power tracking, and use the data power tracking for training the network model, which has more obvious advantages than using the original power tracking for training the network model. To verify the effectiveness of the approach, we evaluated the ATxmega128D4 microcontroller by capturing the power traces generated when implementing AES-128. Experimental results show that network models trained using data power traces outperform network models trained using raw power traces in terms of classification accuracy, training time, cross-subkey recovery key and cross-device recovery key.