Learning to Attack from Electromagnetic Emanation

摘要：

　　Sensitive information processed by the circuitry in electronic security devices can be leaked via physical characteristics of the device,such as power consumption,electromagnetic (EM) emanation,timing,etc.These techniques are known as Side-Channel Attacks (SCA).To date,a significant amount of research has been carried out into side channel attacks,which uses statistical processing techniques to analyse the information leaked from the device.This work formalized the problem of studying the relation between EM emanation and encryption key as a supervised learning task.The considered technique is Support Vector Machine (SVM).The chosen side channel is the EM emanation and the target is a software implementation of the Data Encryption Standard (DES).In this study,several feature selection techniques are compared in a real experimental setting.Our promising results regarding the DES encryption scheme confirms the importance of adopting SVM in cryptanalysis and the effectiveness of our approach in feature selection.

关键词： Electromagnetic SOST Principal component analysis SVM DES

作者: Biao Liu Huamin Feng Zheng Yuan Yougang Gao

作者单位: School of Electronic Engineering,Beijing University of Posts and Telecommunications Beijing,China;Ke Key Laboratory of Information Security,Beijing Electronic and Technology Institute Beijing,China School of Electronic Engineering,Beijing University of Posts and Telecommunications Beijing,China

会议类型: 国际会议

会议名称: Asia-Pacific Conference on Environmental Electromagnetics (2012年第六届亚太环境电磁学会议(CEEM 2012))

会议地点: 上海

会议语种:英文

页码: 202-205

在线出版日期: 2012-11-06（万方平台首次上网日期，不代表论文的发表时间）