边信道攻击和学习向量量化

Ehsan SAEEDI; Yinan KONG; Md. Selim HOSSAIN

doi:10.1631/FITEE.1500460

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边信道攻击和学习向量量化

常规文章 | Updated：2022-05-19

- 边信道攻击和学习向量量化
- Side-channel attacks and learning-vector quantization
- 信息与电子工程前沿（英文） 2017年18卷第4期页码：511-518
- Affiliations：
  
  Department of Engineering, Macquarie University, Sydney, Australia
- Author bio：
  
  E-mail:saeedi.ehsan@gmail.com
- Funds：
- DOI：10.1631/FITEE.1500460
  中图分类号： TP309
- 收稿：2015-12-19，
  
  录用：2016-2-28，
  
  纸质出版：2017-04
- Accepted：
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Ehsan SAEEDI, Yinan KONG, Md. Selim HOSSAIN. 边信道攻击和学习向量量化[J]. 信息与电子工程前沿（英文）, 2017,18(4):511-518.

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.
Ehsan SAEEDI, Yinan KONG, Md. Selim HOSSAIN. 边信道攻击和学习向量量化[J]. 信息与电子工程前沿（英文）, 2017,18(4):511-518. DOI： 10.1631/FITEE.1500460.

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. DOI： 10.1631/FITEE.1500460.

摘要

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。

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.

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