Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more satisfactory attack results. Most of them con-centrate on improving network architectures or putting forward novel metrics, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We utilize mixup to generate new traces and perform Correlation Power Analysis for generated traces and original traces. The analysis reveals that the leakage location and leakage intensity between them are consistent. In view of this observation, we consider it feasible to add generated traces to the original profiling set. Our verifying experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the whole set, mixup can almost reduce required attacking traces to half. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and surprisingly discover that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results.