Comparative study of power analysis attacks based on template and KNN algorithm

Jifang JIN; Chengyuan LIU; Xiaohong FAN; Xiaoyi DUAN; Jiayu LIU

doi:10.11959/j.issn.1000-0801.2022080

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Comparative study of power analysis attacks based on template and KNN algorithm

Research and Development | 更新时间：2024-06-05

- Comparative study of power analysis attacks based on template and KNN algorithm
- Telecommunications Science Vol. 38, Issue 4, Pages: 121-129(2022)
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- DOI：10.11959/j.issn.1000-0801.2022080
  CLC： TM344.1
- Published Online：2022-04，
  
  Published：20 April 2022
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Jifang JIN, Chengyuan LIU, Xiaohong FAN, et al. Comparative study of power analysis attacks based on template and KNN algorithm[J]. Telecommunications science, 2022, 38(4): 121-129.
DOI：

Jifang JIN, Chengyuan LIU, Xiaohong FAN, et al. Comparative study of power analysis attacks based on template and KNN algorithm[J]. Telecommunications science, 2022, 38(4): 121-129. DOI： 10.11959/j.issn.1000-0801.2022080.

摘要

能量分析攻击至今仍是针对密码芯片最具威胁的攻击方法之一，针对传统的模板分析攻击和KNN算法的攻击进行对比研究，对比模板攻击和机器学习中的KNN优缺点。首先对皮尔逊相关系数、互信息和最大信息系数、距离相关系数 3 种降维方法进行了研究；然后对比了相同数量功耗曲线下，特征点数量对两种能量分析的成功率等性能的影响；同时研究了不同降维技术在相同功耗曲线数量和不同功耗曲线数量时对两种能量分析攻击的影响。结果表明，模板攻击在运行速度、占用内存方面优于KNN算法攻击，而在攻击成功率和鲁棒性方面，KNN算法攻击具有更好的表现。

Abstract

Power analysis attack is still the most threatening type of side channel attack on cryptographic hardware.The template analysis attack with the attack of KNN algorithm was compared.Firstly

three dimensionality reduction methods of Pearson correlation coefficient

mutual information and maximum information coefficient and distance correlation coefficient were studied.Then

the effects of the number of feature points on the attack success rate of the two power analysis attacks under the same number of power consumption curves were compared.At the same time

the effects of different dimensionality reduction techniques on the two power analysis attacks when the number of power curves is the same and different.The results show that the template attack is better than the KNN algorithm attack in running speed

memory occupation and robustness

and the KNN algorithm attack has better performance in attack success rate.

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references

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