Adaptive chosen-plaintext correlation power analysis

Wenjing Hu, Liji Wu, An Wang, Xinjun Xie, Zhihui Zhu, Shun Luo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

Yongdae K ea al. poposed biasing power traces to improve correlation in power analysis attack in 2010. However this method abandons large numbers of power traces which is unreasonable in comparison with traditional CPA. In this paper, the traces acquirement process is divided into two stages. In the first stage, some plaintexts are chosen randomly and two most probable key byte candidates are recovered. In the second stage, we adaptively choose specific plaintexts corresponding to the traces with high signal-to-noise ratio, encrypt them, and acquire the second batch of traces. So the attack can be finished with fewer traces. According to our experiments on AT89S52 software implementation of AES, getting the same success rate 0.955, our adaptive chosen-plaintext CPA only requires 78.9% traces of traditional CPA. Our proposal can be implemented by automatic software through two interactions with the AT89S52.

Original languageEnglish
Title of host publicationProceedings - 2014 10th International Conference on Computational Intelligence and Security, CIS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages494-498
Number of pages5
ISBN (Electronic)9781479974344
DOIs
Publication statusPublished - 20 Jan 2015
Externally publishedYes
Event10th International Conference on Computational Intelligence and Security, CIS 2014 - Kunming, Yunnan, China
Duration: 15 Nov 201416 Nov 2014

Publication series

NameProceedings - 2014 10th International Conference on Computational Intelligence and Security, CIS 2014

Conference

Conference10th International Conference on Computational Intelligence and Security, CIS 2014
Country/TerritoryChina
CityKunming, Yunnan
Period15/11/1416/11/14

Keywords

  • Adaptive chosen-plaintext attack
  • Advanced encryption standard
  • Correlation power analysis
  • Hamming weight power model

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