The Cognition Is Not Enough: Another Look on Existing Interesting Points Chosen Methods

FAN Guangjun; ZHOU Yongbin; ZHANG Hailong; FENG Dengguo

doi:10.1049/cje.2017.01.030

Volume 26 Issue 2

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FAN Guangjun, ZHOU Yongbin, ZHANG Hailong, et al., “The Cognition Is Not Enough: Another Look on Existing Interesting Points Chosen Methods,” Chinese Journal of Electronics, vol. 26, no. 2, pp. 416-423, 2017, doi: 10.1049/cje.2017.01.030

Citation:

FAN Guangjun, ZHOU Yongbin, ZHANG Hailong, et al., “The Cognition Is Not Enough: Another Look on Existing Interesting Points Chosen Methods,” Chinese Journal of Electronics, vol. 26, no. 2, pp. 416-423, 2017, doi: 10.1049/cje.2017.01.030

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The Cognition Is Not Enough: Another Look on Existing Interesting Points Chosen Methods

doi: 10.1049/cje.2017.01.030

1.
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;
2.
State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

Funds: This work is supported by the National Natural Science Foundation of China (No.91118006), the National Natural Science Foundation of China (No.61472416, No.61272478), and the National Key Scientific and Technological Project (No.2014ZX01032401-001).

Received Date: 2015-01-05
Rev Recd Date: 2015-03-10
Publish Date: 2017-03-10

Abstract

Abstract

For classical template attacks, many papers suggested a guideline of choosing interesting points which is still not proven. Up to now, many different methods of choosing interesting points were introduced. However, it is still unclear that which approach will lead to the best classification performance for template attacks. We comprehensively evaluate and compare the classification performance of template attacks when using different methods of choosing interesting points. Evaluation results show that the Correlation power analysis based method and the Sum of squared pairwise t-differences based method will lead to the best classification performance. We find that some methods of choosing interesting points provide the same results in the same circumstance. Finally, we correctly prove the guideline of choosing interesting points is correct by presenting a new way of conducting template attacks.
- Side-channel attacks,
- Power analysis attacks,
- Template attacks,
- Interesting points

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References(16)

References

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