Paper 2013/274

A time series approach for profiling attack

Liran Lerman, Gianluca Bontempi, Souhaib Ben Taieb, and Olivier Markowitch

Abstract

The goal of a profiling attack is to challenge the security of a cryptographic device in the worst case scenario. Though template attack are 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 time series nature of the power traces. 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.