Paper 2018/004
On the Performance of Convolutional Neural Networks for Side-channel Analysis
Stjepan Picek, Ioannis Petros Samiotis, Annelie Heuser, Jaehun Kim, Shivam Bhasin, and Axel Legay
Abstract
In this paper, we ask a question whether convolutional neural networks are more suitable for SCA scenarios than some other machine learning techniques, and if yes, in what situations. Our results point that convolutional neural networks indeed outperforms machine learning in several scenarios when considering accuracy. Still, often there is no compelling reason to use such a complex technique. In fact, if comparing techniques without extra steps like preprocessing, we see an obvious advantage for convolutional neural networks only when the level of noise is small, and the number of measurements and features is high. The other tested settings show that simpler machine learning techniques, for a significantly lower computational cost, perform similar or even better. The experiments with the guessing entropy metric indicate that simpler methods like Random forest or XGBoost perform better than convolutional neural networks for the datasets we investigated. Finally, we conduct a small experiment that opens the question whether convolutional neural networks are actually the best choice in side-channel analysis context since there seems to be no advantage in preserving the topology of measurements.
Metadata
- Available format(s)
-
PDF
- Publication info
- Preprint.
- Keywords
- Side-channel analysisMachine learningDeep learningConvolutional Neural Networks
- Contact author(s)
- picek stjepan @ gmail com
- History
- 2018-05-20: revised
- 2018-01-02: received
- See all versions
- Short URL
- https://ia.cr/2018/004
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2018/004,
author = {Stjepan Picek and Ioannis Petros Samiotis and Annelie Heuser and Jaehun Kim and Shivam Bhasin and Axel Legay},
title = {On the Performance of Convolutional Neural Networks for Side-channel Analysis},
howpublished = {Cryptology {ePrint} Archive, Paper 2018/004},
year = {2018},
url = {https://eprint.iacr.org/2018/004}
}