A Data Augmentation Method for Side-Channel Attacks on Cryptographic Integrated Circuits

Abstract

The leaked signals, including electromagnetic, power, timing, and temperature generated during the operation of cryptographic devices, contain highly correlated key value information, leading to security vulnerabilities. In practical operations, due to information collection conditions and time limitations, attackers can only obtain limited valid data. At the same time, the attacker’s data environment cannot be ideal, and noise can affect the acquisition of valid information. Therefore, to improve the effectiveness of obtaining key values from side-channel information analysis in cryptographic devices, we proposed a data augmentation method based on cycle-consistent generative adversarial networks named EME-CycleGAN. By using generators and discriminators, new data are generated to expand the original electromagnetic information dataset, aiming for better modeling effects. For evaluating the data augmentation effect on side-channel electromagnetic signals, we employed the Kolmogorov–Smirnov test to characterize the original and generated data, serving as the evaluation standard for our network model and work. We utilize the existing data to model and verify side-channel attacks, evaluating the impact of the generated information on the overall experimental results. The proposed structure consists of three main parts: side-channel information acquisition, data verification analysis, and determination of attack positions. Experimental results demonstrate that effective attacks on encryption algorithms can be achieved under small-sample dataset conditions.

Keywords

• side channel attack
• electromagnetic signal
• data augmentation
• generative adversarial network
• Kolmogorov–Smirnov test