Cryptology ePrint Archive: Report 2020/039

Online Performance Evaluation of Deep Learning Networks for Side-Channel Analysis

Damien Robissout and Gabriel Zaid and Brice Colombier and Lilian Bossuet and Amaury Habrard

Abstract: Deep learning based side-channel analysis has seen a rise in popularity over the last few years. A lot of work is done to understand the inner workings of the neural networks used to perform the attacks and a lot is still left to do. However, finding a metric suitable for evaluating the capacity of the neural networks is an open problem that is discussed in many articles. We propose an answer to this problem by introducing an online evaluation metric dedicated to the context of side-channel analysis and use it to perform early stopping on existing convolutional neural networks found in the literature. This metric compares the performance of a network on the training set and on the validation set to detect underfitting and overfitting. Consequently, we improve the performance of the networks by finding their best training epoch and thus reduce the number of traces used by 30%. The training time is also reduced for most of the networks considered.

Category / Keywords: secret-key cryptography / Side-Channel Attacks, Metrics, Deep Learning, Underfitting, Overfitting

Date: received 14 Jan 2020, last revised 15 Jan 2020

Contact author: damien robissout at univ-st-etienne fr

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Version: 20200115:125500 (All versions of this report)

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