An efficient leakage characterization method for profiled power analysis attacks

Authors:
Hailong Zhang;Yongbin Zhou;Dengguo Feng
Affiliations:
State Key Laboratory of Information Security, Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China,Graduate University of Chinese Academy of Sciences, Beijing, P.R. China;State Key Laboratory of Information Security, Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China;State Key Laboratory of Information Security, Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
ICISC'11 Proceedings of the 14th international conference on Information Security and Cryptology
Year:
2011

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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.