Cryptology ePrint Archive: Report 2020/1134

Back To The Basics: Seamless Integration of Side-Channel Pre-processing in Deep Neural Networks

Yoo-Seung Won and Xiaolu Hou and Dirmanto Jap and Jakub Breier and Shivam Bhasin

Abstract: Deep learning approaches have become popular for Side-Channel Analysis (SCA) in the recent years. Especially Convolutional Neural Networks (CNN) due to their natural ability to overcome jitter-based as well as masking countermeasures. However, most efforts have focused on finding optimal architecture for a given dataset and bypass the need for trace pre-processing. However, trace pre-processing is a long studied topic and several proven techniques exist in the literature. There is no straightforward manner to integrate those techniques into deep learning based SCA. In this paper, we propose a generic framework which allows seamless integration of multiple, user defined pre-processing techniques into the neural network architecture. The framework is based on Multi-scale Convolutional Neural Networks (MCNN) that were originally proposed for time series analysis. MCNN are composed of multiple branches that can apply independent transformation to input data in each branch to extract the relevant features and allowing a better generalization of the model. In terms of SCA, these transformation can be used for integration of pre-processing techniques, such as phase-only correlation, principal component analysis, alignment methods etc. We present successful results on publicly available datasets. Our findings show that it is possible to design a network that can be used in a more general way to analyze side-channel leakage traces and perform well across datasets.

Category / Keywords: secret-key cryptography / Multi-scale convolutional neural networks and MCNN and Side-channel attacks and Deep learning

Date: received 17 Sep 2020, last revised 21 Sep 2020

Contact author: yooseung won at ntu edu sg,jbreier@jbreier com,houxiaolu email@gmail com,djap@ntu edu sg,sbhasin@ntu edu sg

Available format(s): PDF | BibTeX Citation

Version: 20200921:135037 (All versions of this report)

Short URL: ia.cr/2020/1134


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