Macquarie University
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Side-channel attacks on elliptic curve cryptosystems based on machine learning techniques

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posted on 2022-03-28, 02:01 authored by Seyed Ehsan Saeedi Shahri
This thesis presents a promising approach to Side-channel attacks on the cryptosystems based on elliptic curve cryptography (ECC). This approach is based on machine learning analysis in characterisation of side-channel information. The original contribution of this thesis is to verify the performance of machine-learning techniques in terms of neural networks (NN), support vector machines (SVM) and principal component analysis (PCA). In this project, PCA is used as a powerful algorithm in the preprocessing stage to decrease the computational complexity of the input dataset, while SVM and NN are utilised as efficient multi-class classiffiers to recognise and classify different patterns of side-channel information. In order to investigate the proposed method, an experiment based on the power consumption and electromagnetic emission of an field-programmable gate array (FPGA) implementation of ECC was conducted. Regarding our experimental results based on an FPGA implementation of ECC, PCA can be used as a strong preprocessing stage to reduce the signal-noise ratio, data-set dimension and algorithm complexity. In addition, after verifying the performance of different techniques and specifications such as kernel functions, neural-network architect and parameters, we inferred that the most efficient machine-learning techniques for side-channel information characterisation are LVQ neural network (with a number of hidden layers between 90 and 100), and SVM with Gaussian RBF kernel function with parameter p value of 5 and 50 for CS and M-SVM2 SVM models respectively with about 80 to 85 % accuracy.


Table of Contents

1. Introduction -- 2. Overview of side-channel cryptananlysis -- 3. Elliptic-curve cryptosystem implementation -- 4. Pre-processing stage -- 5. Support-vector machine as a side-channel classifier -- 6. Neural networks as side-channel information classifiers -- 7. Thesis conclusion and recommendations for future work.


Bibliography: pages 117-128 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


PhD, Macquarie University, Faculty of Science and Engineering, Department of Engineering

Department, Centre or School

Department of Engineering

Year of Award


Principal Supervisor

Yinan Kong

Additional Supervisor 1

Michael Heimlich


Copyright Seyed Ehsan Saeedi Shahri 2016. Copyright disclaimer:




1 online resource (xxii, 128 pages) illustrations (some colour)

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