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Side-Channel Information Characterisation Based on Cascade-Forward Back-Propagation Neural Network

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Abstract

Traditional cryptanalysis assumes that an adversary only has access to input and output pairs, but has no knowledge about internal states of the device. However, the advent of side-channel analysis showed that a cryptographic device can leak critical information. In this circumstance, Machine learning is known as a powerful and promising method of analysing of side-channel information. In this paper, an experimental investigation on a FPGA implementation of elliptic curve cryptography (ECC) was conducted to explore the efficiency of side-channel information characterisation based on machine learning techniques. In this work, machine learning is used in terms of principal component analysis (PCA) for the preprocessing stage and a Cascade-Forward Back-Propagation Neural Network (CFBP) as a multi-class classifier. The experimental results show that CFBP can be a promising approach in characterisation of side-channel information.

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Authors and Affiliations

  1. Department of Engineering, Macquarie University, Sydney, NSW, 2109, Australia

    Ehsan Saeedi, Md Selim Hossain & Yinan Kong

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  1. Ehsan Saeedi
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  2. Md Selim Hossain
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  3. Yinan Kong
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Correspondence to Ehsan Saeedi.

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Responsible Editor: V. D. Agrawal

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Saeedi, E., Hossain, M.S. & Kong, Y. Side-Channel Information Characterisation Based on Cascade-Forward Back-Propagation Neural Network. J Electron Test 32, 345–356 (2016). https://doi.org/10.1007/s10836-016-5590-4

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  • DOI: https://doi.org/10.1007/s10836-016-5590-4

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