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CLC number: TP309

On-line Access: 2017-04-12

Received: 2015-12-19

Revision Accepted: 2016-02-28

Crosschecked: 2017-03-28

Cited: 0

Clicked: 2273

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ehsan Saeedi

http://orcid.org/0000-0002-0879-113X

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.4 P.511-518

http://doi.org/10.1631/FITEE.1500460


Side-channel attacks and learning-vector quantization


Author(s):  Ehsan Saeedi, Yinan Kong, Md. Selim Hossain

Affiliation(s):  Department of Engineering, Macquarie University, Sydney, Australia

Corresponding email(s):   saeedi.ehsan@gmail.com

Key Words:  Side-channel attacks, Elliptic curve cryptography, Multi-class classification, Learning vector quantization


Ehsan Saeedi, Yinan Kong, Md. Selim Hossain. Side-channel attacks and learning-vector quantization[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 511-518.

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Abstract: 
The security of cryptographic systems is a major concern for cryptosystem designers, even though cryptography algorithms have been improved. side-channel attacks, by taking advantage of physical vulnerabilities of cryptosystems, aim to gain secret information. Several approaches have been proposed to analyze side-channel information, among which machine learning is known as a promising method. Machine learning in terms of neural networks learns the signature (power consumption and electromagnetic emission) of an instruction, and then recognizes it automatically. In this paper, a novel experimental investigation was conducted on field-programmable gate array (FPGA) implementation of elliptic curve cryptography (ECC), to explore the efficiency of side-channel information characterization based on a learning vector quantization (LVQ) neural network. The main characteristics of LVQ as a multi-class classifier are that it has the ability to learn complex non-linear input-output relationships, use sequential training procedures, and adapt to the data. Experimental results show the performance of multi-class classification based on LVQ as a powerful and promising approach of side-channel data characterization.

边信道攻击和学习向量量化

概要:尽管加密算法已得到改进,加密系统的安全性仍然是密码系统设计者关注的重点。边信道攻击可利用加密系统的物理漏洞来获取秘密信息。目前提出的多种边信道信息分析方法中,机器学习被认为是一种有前景的方法。基于神经网络的机器学习可获得指令标志(功耗与电磁辐射),并自动识别。本文对椭圆曲线加密(Elliptic curve cryptography, ECC)的现场可编程门阵列(field-programmable gate array, FPGA)实现展开了新的实验研究,探讨了基于学习向量量化(Learning vector quantization, LVQ)神经网络的边信道信息表征的效率。LVQ作为多类分类器的主要特点是它具有学习复杂非线性输入-输出关系、使用顺序训练程序和适应数据的能力。实验结果表明基于LVQ的多类分类是边信道数据表征的强大且有前景的方法。

关键词:边信道攻击;椭圆曲线加密;多类分类;学习向量量化

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