Side Channel Analysis Optimization Method Based on Data Preprocessing
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摘要: 电磁侧信道信息具有数据庞杂无序，信噪比低的特征，对侧信道分析的结果存在较大影响。针对电磁侧信道数据的特性，该文提出一种最小相关差值的对齐方法，通过参考信号的自相关函数与待对齐信号的互相关函数之间的相似度来估计延时差值。同时，提出一种K奇异值分解(KSVD)字典学习的降噪方法，交替迭代进行稀疏编码和字典更新来滤除高频噪声。为了验证数据预处理方法对侧信道分析结果的优化效果，设计并搭建了电磁侧信道测评系统，并基于实际芯片进行了近场电磁侧信道信息采集与分析。该文使用所提预处理方法对电磁数据进行对齐与降噪，通过t-test泄露评估与相关性电磁分析，对比最大相关系数对齐与小波降噪方法，能够将侧信道攻击的效率分别提高29.91%和55.23%。Abstract: The electromagnetic side channel information has the characteristics of complexity, disorder and low signal-to-noise ratio, which has a great impact on the results of side channel analysis. Based on the characteristics of electromagnetic data, in this paper, an alignment method using maximum correlation difference is proposed, which estimates the delay based on the similarity between the autocorrelation function of the reference signal and the cross-correlation function of the signal to be aligned. At the same time, a noise reduction method of K Singular Value Decomposition(KSVD) dictionary learning is proposed, which performs alternately sparse coding and dictionary update to filter out high-frequency noise. In order to verify the optimization effect of the data preprocessing method on the side channel analysis results, an electromagnetic side channel evaluation system is designed and built, and the near-field electromagnetic side channel information collection and analysis are carried out based on the actual chip. The proposed preprocessing method is used in this paper to align and reduce noise of electromagnetic data, and through t-test leakage assessment and correlation electromagnetic analysis, the maximum correlation coefficient alignment and wavelet noise reduction methods are compared, which can improve the efficiency of side-channel attacks 29.91% and 55.23%.
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表 1 不同对齐方法的MTD值
对齐方法 A点(x=800 μm, y=300 μm) B点(x=1000 μm, y=200 μm) C点(x=0 μm, y=0 μm) 最大相关系数对齐 960 876 330 最小相关差值对齐 737 614 313 
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表 2 不同降噪方法的MTD值
降噪方法 A点(x=800 μm, y=300 μm) B点(x=1000 μm, y=200 μm) C点(x=0 μm, y=0 μm) 小波降噪 688 429 293 KSVD降噪 308 361 244
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表 3 SNR计算结果(a.u.)
未对齐 对齐 对齐+降噪 A点(x=800 μm, y=300 μm) 0.6322 0.7729 0.8409 B点(x=1000 μm, y=200 μm) 0.5473 0.7671 0.7995 C点(x=0 μm, y=0 μm) 0.5315 0.9084 0.9000 FPGA(x=0 mm, y=0 mm) 0.5606 0.7582 0.7626
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表 4 t-test评估结果
未对齐 对齐 对齐+降噪 A点(x=800 μm, y=300 μm) 4.0437 4.4323 5.0613 B点(x=1000 μm, y=200 μm) 0.5188 4.5141 4.9890 C点(x=0 μm, y=0 μm) 1.2355 6.5763 7.3486
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