Abstract
Correlation Power Analysis (CPA) uses a lot of computational resources for deploying side-channel analysis to power traces. For every guessing key, it searches all data points and all power traces to reveal the secret key implemented in the encryption module. In this work, we propose a novel technique to narrow down the target region for applying CPA to the power traces. As byte-wise operations occur in every clock period, the assumed amplitude value based on the power consumption model and the actual amplitude are most correlated at the points where the peaks are observed. We extract the calculation points of each power trace using signal processing and machine learning techniques, and select the region where the CPA will be applied to. Our proposed approach achieves an approximately 20% improvement in accuracy and requires less computational resources compared to conventional CPA.
| Original language | English |
|---|---|
| Article number | 9430569 |
| Pages (from-to) | 74275-74285 |
| Number of pages | 11 |
| Journal | IEEE Access |
| Volume | 9 |
| DOIs | |
| Publication status | Published - 2021 |
Bibliographical note
Funding Information:This work was supported in part by the Institute of Information and Communications Technology Planning and Evaluation (IITP) Grant by the Korea Government Ministry of Science and ICT (MSIT) under Grant 2020-0-00913 (Study on wireless covert channel risk verification), and in part by the Special Research Grant from Korea university under Grant K2006401.
Publisher Copyright:
© 2013 IEEE.
Keywords
- AES
- Side-channel analysis
- correlation power analysis
- k-means algorithm
- peak detection
- simple power analysis
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)
- Engineering(all)