Machine-learning-based side-channel evaluation of elliptic-curve cryptographic FPGA processor

Naila Mukhtar*, Mohamad Ali Mehrabi, Yinan Kong, Ashiq Anjum

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
16 Downloads (Pure)


Security of embedded systems is the need of the hour. A mathematically secure algorithm runs on a cryptographic chip on these systems, but secret private data can be at risk due to side-channel leakage information. This research focuses on retrieving secret-key information, by performing machine-learning-based analysis on leaked power-consumption signals, from Field Programmable Gate Array (FPGA) implementation of the elliptic-curve algorithm captured from a Kintex-7 FPGA chip while the elliptic-curve cryptography (ECC) algorithm is running on it. This paper formalizes the methodology for preparing an input dataset for further analysis using machine-learning-based techniques to classify the secret-key bits. Research results reveal how pre-processing filters improve the classification accuracy in certain cases, and show how various signal properties can provide accurate secret classification with a smaller feature dataset. The results further show the parameter tuning and the amount of time required for building the machine-learning models.

Original languageEnglish
Article number64
Pages (from-to)1-20
Number of pages20
JournalApplied Sciences (Switzerland)
Issue number1
Publication statusPublished - 1 Jan 2019

Bibliographical note

Copyright the Author(s) 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • side-channel analysis
  • power-analysis attack
  • embedded system security
  • machine-learning classification

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