Cryptology ePrint Archive: Report 2017/740

Convolutional Neural Networks with Data Augmentation against Jitter-Based Countermeasures -- Profiling Attacks without Pre-Processing --

Eleonora Cagli and Cécile Dumas and Emmanuel Prouff

Abstract: In the context of the security evaluation of cryptographic implementations, profiling attacks (aka Template Attacks) play a fundamental role. Nowadays the most popular Template Attack strategy consists in approximating the information leakages by Gaussian distributions. Nevertheless this approach suffers from the difficulty to deal with both the traces misalignment and the high dimensionality of the data. This forces the attacker to perform critical preprocessing phases, such as the selection of the points of interest and the realignment of measurements. Some software and hardware countermeasures have been conceived exactly to create such a misalignment. In this paper we propose an end-to-end profiling attack strategy based on the Convolutional Neural Networks: this strategy greatly facilitates the attack roadmap, since it does not require a previous trace realignment nor a precise selection of points of interest. To significantly increase the performances of the CNN, we moreover propose to equip it with the data augmentation technique that is classical in other applications of Machine Learning. As a validation, we present several experiments against traces misaligned by different kinds of countermeasures, including the augmentation of the clock jitter effect in a secure hardware implementation over a modern chip. The excellent results achieved in these experiments prove that Convolutional Neural Networks approach combined with data augmentation gives a very efficient alternative to the state-of-the-art profiling attacks.

Category / Keywords: side channel attacks, machine learning, deep learning, convolutional neural networks, data augmentation, jitter

Original Publication (with minor differences): IACR-CHES-2017

Date: received 28 Jul 2017, last revised 2 Oct 2017

Contact author: eleonora cagli at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20171002:092834 (All versions of this report)

Short URL: ia.cr/2017/740

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