The goal of a profiling attack is to challenge the security of a cryptographic device in the worst case scenario. Though template attack is reputed as the strongest power analysis attack, they effectiveness is strongly dependent on the validity of the Gaussian assumption. This led recently to the appearance of nonparametric approaches, often based on machine learning strategies. Though these approaches outperform template attack, they tend to neglect the potential source of information available in the temporal dependencies between power values. In this paper, we propose an original multi-class profiling attack that takes into account the temporal dependence of power traces. The experimental study shows that the time series analysis approach is competitive and often better than static classification alternatives. © 2013 Springer-Verlag.
CITATION STYLE
Lerman, L., Bontempi, G., Ben Taieb, S., & Markowitch, O. (2013). A time series approach for profiling attack. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8204 LNCS, pp. 75–94). Springer Verlag. https://doi.org/10.1007/978-3-642-41224-0_7
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