Abstract
Electronic devices may undergo attacks going beyond traditional cryptanalysis. Side-channel analysis (SCA) is an alternative attack that exploits information leaking from physical implementations of e.g. cryptographic devices to discover cryptographic keys or other secrets. This work comprehensively investigates the application of a machine learning technique in SCA. The considered technique is a powerful kernel-based learning algorithm: the Least Squares Support Vector Machine (LS-SVM). The chosen side-channel is the power consumption and the target is a software implementation of the Advanced Encryption Standard. In this study, the LS-SVM technique is compared to Template Attacks. The results show that the choice of parameters of the machine learning technique strongly impacts the performance of the classification. In contrast, the number of power traces and time instants does not influence the results in the same proportion. This effect can be attributed to the usage of data sets with straightforward Hamming weight leakages in this first study.
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This work was supported in part by the European Commission’s ECRYPT II NoE (ICT-2007-216676), by the Belgian State’s IAP program P6/26 BCRYPT, by the K.U. Leuven-BOF (OT/06/40) and by the Research Council K.U. Leuven: GOA TENSE (GOA/11/007). Benedikt Gierlichs is a Postdoctoral Fellow of the Fund for Scientific Research, Flanders (FWO).
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Hospodar, G., Gierlichs, B., De Mulder, E. et al. Machine learning in side-channel analysis: a first study. J Cryptogr Eng 1, 293–302 (2011). https://doi.org/10.1007/s13389-011-0023-x
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DOI: https://doi.org/10.1007/s13389-011-0023-x