An Improved Principal Component Analysis for Side-Channel Attacks

被引：1

作者：

Bai, Haoming ^{[1
]}

Gao, Hongling ^{[2
]}

Yu, Shan ^{[2
]}

Zhai, Teng ^{[2
]}

Ma, Ziyang ^{[2
]}

Guo, Qingshuai ^{[2
]}

机构：

[1] Inst Telecommun Sci & Technol, Beijing, Peoples R China

[2] Minist Ind & Informat Technol, China Software Testing Ctr, Integrated Circuit & Reliabil Evaluat Engn Techno, Software & Integrated Circuit Promot Ctr, Beijing, Peoples R China

来源：

2022 IEEE 10TH INTERNATIONAL CONFERENCE ON INFORMATION, COMMUNICATION AND NETWORKS (ICICN 2022) | 2022年

关键词：

side-channel attacks (SCAs); principal component analysis (PCA); data compression; correlation power analysis (CPA); power traces;

D O I：

10.1109/ICICN56848.2022.10006485

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Side-channel attacks (SCAs) often need to collect a lot of power consumption data, and it takes a great deal of time to crack the secret key through these data. Ordinary principal component analysis (PCA) compression techniques consume a lot of time when processing power traces containing a large number of sample points. In this paper, two improved algorithms, absolute value integration PCA (APCA) and maximum value extraction PCA (MPCA), are proposed to optimize PCA. Both can compress the original power traces to about 10%. Compared with PCA, their processing speed is significantly improved. The more sample points, the more obvious the improvement. And through experiments, it is proved that APCA has more advantages than MPCA in the convergence of partial guessing entropy (PGE).

引用

页码：343 / 348

页数：6

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