Cryptology ePrint Archive: Report 2021/197

Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-based SCA

Guilherme Perin and Lichao Wu and Stjepan Picek

Abstract: Deep learning-based side-channel analysis (SCA) represents the de facto standard in the profiling SCA. Still, this does not mean it is trivial to find neural networks that perform well for any setting. Based on the developed neural network architectures, we can distinguish between small neural networks that are easier to tune and less prone to overfitting but can have insufficient capacity to model the data. On the other hand, large neural networks would have sufficient capacity but can overfit and are more difficult to tune. This brings an interesting trade-off between simplicity and performance. This paper proposes using a pruning strategy and recently proposed Lottery TicketHypothesis to improve the deep learning-based SCA. We demonstrate that we can find smaller neural networks that perform on the level of larger networks, where we manage to reduce the number of weights by more than 90% on average. What is more, we obtain neural networks that are smaller than state-of-the-art, and still, we manage to outperform previous top results in a number of settings. Additionally, we show that pruning can help prevent overfitting and the effects of imbalanced data, reaching top attack performance for small networks when larger networks do not manage to break the target at all.

Category / Keywords: applications / Side-channel Analysis, Deep learning, Lottery Ticket Hypothesis, Pruning

Date: received 23 Feb 2021, last revised 19 Apr 2021

Contact author: guilhermeperin7 at gmail com, L Wu-4 at tudelft nl, picek stjepan at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20210419:163621 (All versions of this report)

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