Feed-Forward Back-Propagation Neural Networks in Side-Channel Information Characterization

被引：5

作者：

Saeedi, E. ^{[1
]}

Hossain, M. S. ^{[1
]}

Kong, Y. ^{[1
]}

机构：

[1] Macquarie Univ, Dept Elect Engn, Sydney, NSW 2109, Australia

来源：

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS | 2019年 / 28卷 / 01期

关键词：

Side-channel attacks; multi-class classification; neural networks; principal components analysis; elliptic curve cryptography; ALGORITHM;

D O I：

10.1142/S0218126619500038

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The safety of cryptosystems, mainly based on algorithmic improvement, is still vulnerable to side-channel attacks (SCA) based on machine learning. Multi-class classification based on neural networks and principal components analysis (PCA) can be powerful tools for pattern recognition and classification of side-channel information. In this paper, an experimental investigation was conducted to explore the efficiency of various architectures of feed-forward back-propagation (FFBP) neural networks and PCA against side-channel attacks. The experiment is performed on the data leakage of an FPGA implementation of elliptic curve cryptography (ECC). Our results show that the proposed method is a promising method for SCA with an overall accuracy of 88% correct classification.

引用

页数：18

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