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FITEE
Frontiers of Information Technology & Electronic Engineering  2017, Vol. 18 Issue (4): 511-518    DOI: 10.1631/FITEE.1500460
Regular Paper     
边信道攻击和学习向量量化
Ehsan Saeedi, Yinan Kong, Md. Selim Hossain
Side-channel attacks and learning-vector quantization
Ehsan Saeedi, Yinan Kong, Md. Selim Hossain
Department of Engineering, Macquarie University, Sydney, Australia
 全文: PDF 
摘要： 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    
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.
Key words: Side-channel attacks    Elliptic curve cryptography    Multi-class classification    Learning vector quantization
收稿日期: 2015-12-19 出版日期: 2017-04-12
CLC:  TP309  
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Ehsan Saeedi
Yinan Kong
Md. Selim Hossain

引用本文:

Ehsan Saeedi, Yinan Kong, Md. Selim Hossain. Side-channel attacks and learning-vector quantization. Front. Inform. Technol. Electron. Eng., 2017, 18(4): 511-518.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/FITEE.1500460    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2017/V18/I4/511

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