基于改进小波包分解的相关功耗攻击降噪方法

Denoising Method for Correlation Power Attack Based on Improved Wavelet Packet Decomposition

侧信道攻击中功耗数据纯净度影响功耗攻击效率和密钥破解准确率,通常采用小波变换或小波包变换等降噪方法进行功耗预处理,但小波变换方法在表征数据时易忽略高频信息,而小波包变换方法的降噪阈值不具备普适性。针对上述问题,提出一种将小波包分解与奇异谱分析相结合的相关功耗攻击降噪方法。使用小波包变换方法分解功耗数据,利用奇异谱分析处理低频和高频信息,并根据奇异熵分布趋势自适应地提取功耗信息以提高数据质量。采用SM4算法进行选择明文攻击的实验结果表明,与改进前小波包降噪方法相比,该方法能有效提升功耗数据的信噪比和相关功耗攻击效率,降低密钥破解所需功耗。

In the Side Channel Attack(SCA),the purity of power data seriously affects the efficiency of power attacks and the accuracy of key cracking,so denoising methods including Wavelet Transform(WT)and Wavelet Packet Transform(WPT)are widely used in power consumption preprocessing.However,WT tends to ignore high-frequency information when characterizing data,and the noise reduction threshold of WPT is not universal.To solve the problems,this paper proposes a new denoising method for Correlation Power Attack(CPA),which combines Wavelet Packet Decomposition(WPD)with Singular Spectrum Analysis(SSA).WPT is used to decompose the power consumption data,SSA is used to process the low-frequency and high-frequency information,and power consumption information is extracted adaptively according to the distribution trend of singular entropy to improve data quality.Experimental results of the SM4 algorithm for selective plaintext attacks show that compared with the original wavelet packet denoising method,the proposed method can effectively improve the signal-to-noise ratio of power consumption data and the efficiency of CPA,and reduce the power consumption of key cracking.