Enhancing Deep-Learning Based Side-Channel Analysis Through Simultaneously Multi-Byte Training

Abstract

Abstract Preparing a large number of physical traces is an important first step in Side-Channel Analysis, especially in Deep-Learning based Side-Channel Analysis (DL-SCA). With sufficient training data and a proper modeling algorithm, the secret key of cryptographic devices can be successfully recovered with a small number of attacking data. However, in reality, it may be impossible or difficult, in some threat models, to collect sufficient data due to various resource constraints. In this case, the performance of DL-SCA will be severely decreased. In this work, we propose an easy-to-implement method to achieve an efficient DL-SCA with a small number of training data in the scenario of software-based cryptographic implementations. Our simultaneously multi-byte training method, which trains the model with side-channel leakage characteristics of different byte intermediate values, significantly enhances the robustness and performance of DL-SCA. The simulated experiment shows that our method achieves more robust profiling. The success rate of recovering a secret AES key can be improved by 250% with the same collected data. The results of attacking real-world COTS USIM cards are consistent with the ones of simulation-based counterparts. Compared with state-of-the-art data-augmentation techniques, our results show that the proposed method can achieve the same or even better performance without additional generated training data.