Side-channel information characterisation based on cascade-forward back-propagation neural network

Ehsan Saeedi*, Md Selim Hossain, Yinan Kong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)345-356
Number of pages12
JournalJournal of Electronic Testing: Theory and Applications (JETTA)
Volume32
Issue number3
DOIs
Publication statusPublished - 1 Jun 2016

Keywords

  • Elliptic curve cryptography
  • Multi-class classification
  • Neural networks
  • Principal components analysis
  • Side-channel attacks

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