Side-Channel Attacks using Convolutional Neural Networks

A Study on the performance of Convolutional Neural Networks on side-channel data

Master thesis (2018)

Authors

I.P. Samiotis Electrical Engineering, Mathematics and Computer Science

Contributors

S. Picek (supervisor 1)
J.C.A. van der Lubbe (supervisor 2)
A. Hanjalic (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2018 Ioannis Petros Samiotis
Machine Learning Cybersecurity Deep Learning Convolutional Neural Networks Classification Optimization algorithms Side-Channel Attacks
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Published Date

26-04-2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Side-Channel Attacks, are a prominent type of attacks, used to break cryptographic implementations on a computing system. They are based on information "leaked" by the hardware of a computing system, rather than the encryption algorithm itself. Recent studies showed that Side-Channel Attacks can be performed using Deep Learning models. In this study, we examine the performance of Convolutional Neural Networks, on four different datasets of side- channel data and we compare our models with conventional Machine Learning algorithms and a CNN model from literature. We found that CNNs have the potential to achieve high accuracy performance (99.8%), although their capacity is heavily influenced by the use case. We also found that certain Machine Learning algorithms can outperform CNNs in certain cases, leaving an open debate on the performance gains of the latter.