Abstract
Deep neural networks have become the state-of-the-art method when a profiled side-channel analysis is performed. Their popularity is mostly due to neural nets overcoming some of the drawbacks of “classical” sidßhe need for feature selection or waveform synchronization, in addition to their capability to bypass certain countermeasures like random delays. To design and tune a neural network for side-channel analysis systematically is a complicated task. There exist hyperparameter tuning techniques which can be used in the side-channel analysis context, like Grid Search, but they are not optimal since they usually rely on specific machine learning metrics that cannot be directly linked to e.g. the success of the attack.
We propose a customized version of an existing statistical methodology called Six Sigma for optimizing the deep learning-based side-channel analysis process. We demonstrate the proposed methodology by successfully attacking a masked software implementation of AES.
S. Paguada and U. Rioja—These authors contributed equally to this work.
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Notes
- 1.
The key guessing vector \(\mathbf{g} \) (over \(n_{a}\) power traces) is computed using the log-likelihood principle \( g_{i}=\sum _{j=1}^{n_{a}} \log \left( \hat{p}_{i j}\right) \).
- 2.
In SCA on software AES implementations, is common to target 8-bit intermediate values. In this case, since the size of the keyspace |K| is \(2^8\), the maximum GE value (worst case) is 256.
- 3.
In [48], if we analyze the possible combinations of the specified subset of hyperparameters for Grid search optimization, we obtain \( 3^{2} \cdot 4^{2} \cdot 8^{1} \cdot 7^{1} \cdot 5^{1} = 40\,320 \) possible combinations
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Paguada, S., Rioja, U., Armendariz, I. (2020). Controlling the Deep Learning-Based Side-Channel Analysis: A Way to Leverage from Heuristics. In: Zhou, J., et al. Applied Cryptography and Network Security Workshops. ACNS 2020. Lecture Notes in Computer Science(), vol 12418. Springer, Cham. https://doi.org/10.1007/978-3-030-61638-0_7
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