Cryptology ePrint Archive: Report 2021/944

Systematic Side-channel Analysis of Curve25519 with Machine Learning

Léo Weissbart and Łukasz Chmielewski and Stjepan Picek and Lejla Batina

Abstract: Profiling attacks, especially those based on machine learning, proved to be very successful techniques in recent years when considering the side-channel analysis of symmetric-key crypto implementations. At the same time, the results for implementations of asymmetric-key cryptosystems are very sparse.

This paper considers several machine learning techniques to mount side-channel attacks on two implementations of scalar multiplication on the elliptic curve Curve25519. The first implementation follows the baseline implementation with complete formulae as used for EdDSA in WolfSSl, where we exploit power consumption as a side-channel. The second implementation features several countermeasures, and in this case, we analyze electromagnetic emanations to find side-channel leakage.

Most techniques considered in this work result in potent attacks, and especially the method of choice appears to be convolutional neural networks (CNNs), which can break the first implementation with only a single measurement in the attack phase. The same convolutional neural network demonstrated excellent performance for attacking AES cipher implementations.

Our results show that some common grounds can be established when using deep learning for profiling attacks on very different cryptographic algorithms and their corresponding implementations.

Category / Keywords: Side-channel analysis, Machine learning, Deep learning, Public-key cryptography, Curve25519

Original Publication (in the same form): Journal of Hardware and Systems Security

Date: received 12 Jul 2021

Contact author: lukchmiel at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20210713:163019 (All versions of this report)

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