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Title:
an efficient leakage characterization method for profiled power analysis attacks
Author: Zhang Hailong ; Zhou Yongbin ; Feng Dengguo
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference Name: 14th International Conference on Information Security and Cryptology, ICISC 2011
Conference Date: November 30, 2011 - December 2, 2011
Issued Date: 2012
Conference Place: Seoul, Korea, Republic of
Keyword: Characterization ; Efficiency ; Security of data
Indexed Type: EI
ISSN: 0302-9743
ISBN: 9783642319112
Department: (1) State Key Laboratory of Information Security Institute of Software Chinese Academy of Sciences P.O. Box 8718 Beijing 100190 China; (2) Graduate University of Chinese Academy of Sciences 19A Yuquan Lu Beijing 100049 China
Sponsorship: National Security Research Institute (NSRI); Electronics and Telecommunications Research Institute (ETRI); Korea Internet and Security Agency (KISA); Ministry of Public Administration and Security (MOPAS)
Abstract: In typical Profiled Power Analysis Attacks, like Template Attack (TA) and Stochastic Model based Power Analysis (SMPA), key-recovery efficiency is strongly influenced by the accuracy of characterization in profiling. In order to accurately characterize signals and noises in different times, a large number of power traces is usually needed in profiling. However, a large number of power traces is not always available. In this case, the accuracy of characterization is rapidly degraded, and so it is with the efficiency of subsequent key-recovery. In light of this, we present an efficient Covariance Analysis based Characterization Method (CACM for short) to deal with the problem of more accurate leakage characterization with less power traces. We perform experimental power analysis attacks against an AES software implementation on STC89C52 microcontroller, then conduct a comparative study of the effectiveness of these profiled attacks. The results firmly support the validity and efficiency of our method. © 2012 Springer-Verlag.
English Abstract: In typical Profiled Power Analysis Attacks, like Template Attack (TA) and Stochastic Model based Power Analysis (SMPA), key-recovery efficiency is strongly influenced by the accuracy of characterization in profiling. In order to accurately characterize signals and noises in different times, a large number of power traces is usually needed in profiling. However, a large number of power traces is not always available. In this case, the accuracy of characterization is rapidly degraded, and so it is with the efficiency of subsequent key-recovery. In light of this, we present an efficient Covariance Analysis based Characterization Method (CACM for short) to deal with the problem of more accurate leakage characterization with less power traces. We perform experimental power analysis attacks against an AES software implementation on STC89C52 microcontroller, then conduct a comparative study of the effectiveness of these profiled attacks. The results firmly support the validity and efficiency of our method. © 2012 Springer-Verlag.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/15760
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Zhang Hailong,Zhou Yongbin,Feng Dengguo. an efficient leakage characterization method for profiled power analysis attacks[C]. 见:14th International Conference on Information Security and Cryptology, ICISC 2011. Seoul, Korea, Republic of. November 30, 2011 - December 2, 2011.
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