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http://dx.doi.org/10.13089/JKIISC.2019.29.3.491

Improving Non-Profiled Side-Channel Analysis Using Auto-Encoder Based Noise Reduction Preprocessing  

Kwon, Donggeun (Korea University)
Jin, Sunghyun (Korea University)
Kim, HeeSeok (Korea University)
Hong, Seokhie (Korea University)
Publication Information
Journal of the Korea Institute of Information Security & Cryptology / v.29, no.3, 2019 , pp. 491-501 More about this Journal
Abstract
In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.
Keywords
Side-Channel Analysis; Non-Profiled Attack; Deep Learning; Auto-Encoder; Preprocessing;
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