Cross subkey side channel analysis based on small samples

Author:

Hu Fanliang,Wang Huanyu,Wang Junnian

Abstract

AbstractThe majority of recently demonstrated Deep-Learning Side-Channel Analysis (DLSCA) use neural networks trained on a segment of traces containing operations only related to the target subkey. However, when the size of the training set is limited, as in this paper with only 5K power traces, the deep learning (DL) model cannot effectively learn the internal features of the data due to insufficient training data. In this paper, we propose a cross-subkey training approach that acts as a trace augmentation. We train deep-learning models not only on a segment of traces containing the SBox operation of the target subkey of AES-128 but also on segments for other 15 subkeys. Experimental results show that the accuracy of the subkey combination training model is $$28.20\%$$ 28.20 % higher than that of the individual subkey training model on traces captured in the microcontroller implementation of the STM32F3 with AES-128. And validation is performed on two additional publicly available datasets. At the same time, the number of traces that need to be captured when the model is trained is greatly reduced, demonstrating the effectiveness and practicality of the method.

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference25 articles.

1. Daemen, J. & Rijmen, V. The Design of Rijndael: AES—The Advanced Encryption Standard (Springer, 2002).

2. Kocher, P., Jaffe, J. & Jun, B. Differential power analysis. In Annual International Cryptology Conference 388–397 (Springer, 1999).

3. Mangard, S., Oswald, E. & Popp, T. Power Analysis Attacks: Revealing the Secrets of Smart Cards Vol. 31 (Springer Science & Business Media, 2008).

4. Genkin, D., Shamir, A. & Tromer, E. Acoustic cryptanalysis. J. Cryptol. 30, 392–443 (2017).

5. Wang, R., Wang, H. & Dubrova, E. Far field em side-channel attack on aes using deep learning. In Proceedings of the 4th ACM Workshop on Attacks and Solutions in Hardware Security, pp. 35–44 (2020).

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