Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach

Naila Mukhtar*, Yinan Kong

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

Abstract

Despite having a secure algorithm running on a cryptographic chip, in an embedded system device on the network, secret private data is still vulnerable due to Side-Channel leakage information. In this paper, we have focused on retrieving secret-key information obtained from one of the Side Channels, namely Electromagnetic radiation signals. We have captured leaked Electromagnetic signals from a Kintex-7 FPGA, while AES is running over it, and analyzed them using machine and deep-learning based algorithms to classify each bit of the key. Moreover, we aim to analyze the effect of having different signal properties as features in these classification algorithms. The results will help in defining which features give maximum information about the captured signal, hence leading to key recovery.

Original languageEnglish
Title of host publicationFuture network systems and security
Subtitle of host publication4th International Conference, FNSS 2018 Paris, France, July 9–11, 2018 proceedings
EditorsRobin Doss, Selwyn Piramuthu, Wei Zhou
Place of PublicationCham
PublisherSpringer, Springer Nature
Pages80-92
Number of pages13
ISBN (Electronic)9783319944210
ISBN (Print)9783319944203
DOIs
Publication statusPublished - 2018
Event4th International Conference on Future Network Systems and Security, FNSS 2018 - Paris, France
Duration: 9 Jul 201811 Jul 2018

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer Nature
Volume878
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference4th International Conference on Future Network Systems and Security, FNSS 2018
CountryFrance
CityParis
Period9/07/1811/07/18

Keywords

  • Side-Channel analysis
  • Embedded system security
  • Signal-processing
  • Machine-learning classification
  • Neural-network classification

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  • Cite this

    Mukhtar, N., & Kong, Y. (2018). Secret key classification based on electromagnetic analysis and feature extraction using machine-learning approach. In R. Doss, S. Piramuthu, & W. Zhou (Eds.), Future network systems and security: 4th International Conference, FNSS 2018 Paris, France, July 9–11, 2018 proceedings (pp. 80-92). (Communications in Computer and Information Science; Vol. 878). Cham: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-94421-0_6