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

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。

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.