MDASCA: An Enhanced Algebraic Side-Channel Attack for Error Tolerance and New Leakage Model Exploitation
Algebraic side-channel attack (ASCA) is a powerful cryptanalysis technique different from conventional side-channel attacks. This paper studies ASCA from three aspects: enhancement, analysis and application. To enhance ASCA, we propose a generic method, called Multiple Deductions-based ASCA (MDASCA)...
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Published in | Constructive Side-Channel Analysis and Secure Design pp. 231 - 248 |
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Main Authors | , , , , , , |
Format | Book Chapter |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2012
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Algebraic side-channel attack (ASCA) is a powerful cryptanalysis technique different from conventional side-channel attacks. This paper studies ASCA from three aspects: enhancement, analysis and application. To enhance ASCA, we propose a generic method, called Multiple Deductions-based ASCA (MDASCA), to cope the multiple deductions caused by inaccurate measurements or interferences. For the first time, we show that ASCA can exploit cache leakage models. We analyze the attacks and estimate the minimal amount of leakages required for a successful ASCA on AES under different leakage models. In addition, we apply MDASCA to attack AES on an 8-bit microcontroller under Hamming weight leakage model, on two typical microprocessors under access driven cache leakage model, and on a 32-bit ARM microprocessor under trace driven cache leakage model. Many better results are achieved compared to the previous work. The results are also consistent with the theoretical analysis. Our work shows that MDASCA poses great threats with its excellence in error tolerance and new leakage model exploitation. |
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Bibliography: | This work was supported in part by the National Natural Science Foundation of China under the grants 60772082 and 61173191, and US National Science Foundation under the grant CNS-0644188. |
ISBN: | 9783642299117 3642299113 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-642-29912-4_17 |