基于模板和KNN算法的能量分析攻击对比研究

靳济方; 刘承远; 范晓红; 段晓毅; 刘嘉瑜

doi:10.11959/j.issn.1000-0801.2022080

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基于模板和KNN算法的能量分析攻击对比研究

研究与开发 | 更新时间：2024-06-05

- 基于模板和KNN算法的能量分析攻击对比研究
- Comparative study of power analysis attacks based on template and KNN algorithm
- 电信科学 2022年38卷第4期页码：121-129
- 作者机构：
- 作者简介：
  
  [ "靳济方（1972− ），女，北京电子科技学院电子与通信工程系副教授，主要研究方向为电路与系统安全" ]
  [ "刘承远（1999− ）,男，北京电子科技学院硕士生，主要研究方向为信息安全" ]
  [ "范晓红（1979− ），女，北京电子科技学院电子与通信工程系讲师，主要研究方向为信息安全" ]
  [ "段晓毅（1979− ），男，北京电子科技学院电子与通信工程系副教授，主要研究方向为信息安全" ]
  [ "刘嘉瑜（1999− ），女，北京电子科技学院在读，主要研究方向为信息安全" ]
- 基金信息：
  
  北京市高精尖学科建设基金资助项目;The High-Tech Discipline Construction Funds of China(20210032Z0401)
- DOI：10.11959/j.issn.1000-0801.2022080
  中图分类号： TM344.1
- 网络出版日期：2022-04，
  
  纸质出版日期：2022-04-20
- 稿件说明：
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靳济方, 刘承远, 范晓红, 等. 基于模板和KNN算法的能量分析攻击对比研究[J]. 电信科学, 2022,38(4):121-129.

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.
靳济方, 刘承远, 范晓红, 等. 基于模板和KNN算法的能量分析攻击对比研究[J]. 电信科学, 2022,38(4):121-129. DOI： 10.11959/j.issn.1000-0801.2022080.

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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Keywords

references

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