Feed-forward back-propagation neural networks in side-channel information characterization

E. Saeedi*, M. S. Hossain, Y. Kong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The safety of cryptosystems, mainly based on algorithmic improvement, is still vulnerable to side-channel attacks (SCA) based on machine learning. Multi-class classification based on neural networks and principal components analysis (PCA) can be powerful tools for pattern recognition and classification of side-channel information. In this paper, an experimental investigation was conducted to explore the efficiency of various architectures of feed-forward back-propagation (FFBP) neural networks and PCA against side-channel attacks. The experiment is performed on the data leakage of an FPGA implementation of elliptic curve cryptography (ECC). Our results show that the proposed method is a promising method for SCA with an overall accuracy of 88% correct classification.

Original languageEnglish
Article number1950003
Pages (from-to)1-18
Number of pages18
JournalJournal of Circuits, Systems and Computers
Issue number1
Publication statusPublished - Jan 2019


  • Side-channel attacks
  • multi-class classi¯cation
  • neural networks
  • principal components analysis
  • elliptic curve cryptography


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