Electromagnetic side_channel attack based on PSO Directed Acyclic Graph SVM

doi:10.1016/S1005-8885(15)60674-4

中国邮电高校学报(英文) ›› 2015, Vol. 22 ›› Issue (5): 10-15.doi: 10.1016/S1005-8885(15)60674-4

• Cyber Security • 上一篇下一篇

Electromagnetic side_channel attack based on PSO Directed Acyclic Graph SVM

李端^1,2,张洪欣¹,李强¹,Zhao Xinjie³, He Pengfei⁴

1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China 3. Institute of North Electronic Equipment, Beijing 100191,China 4. School of Opto-electronic Information Science and Technology, Yantai University, Yantai 264005, China

收稿日期:2015-04-13 修回日期:2015-09-20 出版日期:2015-10-30 发布日期:2015-10-30
通讯作者: 张洪欣 E-mail:hongxinzhang@263.net
基金资助:
太赫兹辐射源设计和波谱整形技术研究;基于能效的线性调频超宽带无线体域网数据传输及系统优化

Electromagnetic side_channel attack based on PSO Directed Acyclic Graph SVM

1. School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China 2. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China 3. Institute of North Electronic Equipment, Beijing 100191,China 4. School of Opto-electronic Information Science and Technology, Yantai University, Yantai 264005, China

Received:2015-04-13 Revised:2015-09-20 Online:2015-10-30 Published:2015-10-30
Contact: Zhang Hongxin E-mail:hongxinzhang@263.net
Supported by:
The work was supported by the National Natural Science Foundation of China (61571063, 61202399, 61171051).

摘要/Abstract

摘要：

Machine learning has a powerful potential for performing the template attack (TA) of cryptographic device. To improve the accuracy and time consuming of electromagnetic template attack (ETA), a multi-class directed acyclic graph support vector machine (DAGSVM) method is proposed to predict the Hamming weight of the key. The method needs to generate K(K 1)/2 binary support vector machine (SVM) classifiers and realizes the K-class prediction using a rooted binary directed acyclic graph (DAG) testing model. Further, particle swarm optimization (PSO) is used for optimal selection of DAGSVM model parameters to improve the performance of DAGSVM. By exploiting the electromagnetic emanations captured while a chip was implementing the RC4 algorithm in software, the computation complexity and performance of several multi-class machine learning methods, such as DAGSVM, one-versus-one (OVO)SVM, one-versus-all (OVA)SVM, Probabilistic neural networks (PNN), K-means clustering and fuzzy neural network (FNN) are investigated. In the same scenario, the highest classification accuracy of Hamming weight for the key reached 100%, 95.33%, 85%, 74%, 49.67% and 38% for DAGSVM, OVOSVM, OVASVM, PNN, K-means and FNN, respectively. The experiment results demonstrate the proposed model performs higher predictive accuracy and faster convergence speed.

关键词:

directed acyclic graph support vector machine (DAGSVM)|particle swarm optimization (PSO)|side-channel attack|electromagnetic emanations

Abstract:

Key words:

directed acyclic graph support vector machine (DAGSVM)|particle swarm optimization (PSO)|side-channel attack|electromagnetic emanations

中图分类号:

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Electromagnetic side_channel attack based on PSO Directed Acyclic Graph SVM

Electromagnetic side_channel attack based on PSO Directed Acyclic Graph SVM

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