摘要
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.
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。
