Abstract
Side-channel attacks challenge the security of cryptographic devices. One of the widespread countermeasures against these attacks is the masking approach. In 2012, Nassar et al. [21] presented a new lightweight (low-cost) Boolean masking countermeasure to protect the implementation of the AES block-cipher. This masking scheme represents the target algorithm of the DPAContest V4 [30]. In this article, we present the first machine learning attack against a masking countermeasure, using the dataset of the DPAContest V4. We succeeded to extract each targeted byte of the key of the masked AES with \(26\) traces during the attacking phase. This number of traces represents roughly twice the number of traces needed compared to an unmasked AES on the same cryptographic device. Finally, we compared our proposal to a stochastic attack and to a strategy based on template attack. We showed that an attack based on a machine learning model reduces the number of traces required during the attacking step with a factor two and four compared respectively to template attack and to stochastic attack when analyzing the same leakage information. A new strategy based on stochastic attack reduces this number to 27.8 traces (in average) during the attack but requires a larger execution time in our setting than a learning model.
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Notes
- 1.
The \(50\) instants are sorted in descending order with respect to their correlation coefficient in absolute value.
- 2.
Each sample of the trace is an 8-bit value. The limit of R - the used program language - is \(2^{31}\) bytes for a matrix.
- 3.
SVM had a radial kernel with a gamma equals to the inverse of the data dimension and a cost of \(1\). RF had 500 trees.
- 4.
The experiments were executed on a MacBook Pro with 2.66 GHz Intel Core 2 Duo, 8 GB 1067 MHz DDR3.
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Lerman, L., Medeiros, S.F., Bontempi, G., Markowitch, O. (2014). A Machine Learning Approach Against a Masked AES. In: Francillon, A., Rohatgi, P. (eds) Smart Card Research and Advanced Applications. CARDIS 2013. Lecture Notes in Computer Science(), vol 8419. Springer, Cham. https://doi.org/10.1007/978-3-319-08302-5_5
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