An automated end-to-end side channel analysis based on probabilistic model

Jeonghwan Hwang, Ji Won Yoon

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, we propose a new automated way to find out the secret exponent from a single power trace. We segment the power trace into subsignals that are directly related to recovery of the secret exponent. The proposed approach does not need the reference window to slide, templates nor correlation coefficients compared to previous manners. Our method detects change points in the power trace to explore the locations of the operations and is robust to unexpected noise addition. We first model the change point detection problem to catch the subsignals irrelevant to the secret and solve this problem with Markov Chain Monte Carlo (MCMC) which gives a global optimal solution. After separating the relevant and irrelevant parts in signal, we extract features from the segments and group segments into clusters to find the key exponent. Using single power trace indicates the weakest power level of attacker where there is a very slight chance of acquiring as many power traces as needed for breaking the key. We empirically show the improvement in accuracy even with presence of high level of noise.

Original languageEnglish
Article number2369
JournalApplied Sciences (Switzerland)
Volume10
Issue number7
DOIs
Publication statusPublished - 2020 Apr 1

Keywords

  • Change point detection
  • Markov chain monte carlo
  • Power analysis
  • Side channel attack

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

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