TITLE:
Information Theoretic Distinguishers for Timing Attacks with Partial Profiles: Solving the Empty Bin Issue
AUTHORS:
Eloi De Chérisey, Sylvain Guilley, Olivier Rioul, Darshana Jayasinghe
KEYWORDS:
Timing Attacks, Profiling Attacks, Dirichlet Priors, Success Rates
JOURNAL NAME:
Journal of Information Security,
Vol.12 No.1,
December
10,
2020
ABSTRACT: In any side-channel attack, it is desirable to exploit all the available
leakage data to compute the distinguisher’s values. The profiling phase is
essential to obtain an accurate leakage model, yet it may not be exhaustive. As
a result, information theoretic distinguishers may come up on previously unseen
data, a phenomenon yielding empty bins. A strict application of the maximum
likelihood method yields a distinguisher that is not even sound. Ignoring empty
bins reestablishes soundness, but seriously limits its performance in terms of
success rate. The purpose of this paper is to remedy this situation. In this
research, we propose six different techniques to improve the performance of information
theoretic distinguishers. We study them
thoroughly by applying them to timing attacks, both with synthetic and real
leakages. Namely, we compare them in terms of success rate, and show that their
performance depends on the amount of profiling, and can be explained by a
bias-variance analysis. The result of our work is that there exist use-cases,
especially when measurements are noisy, where our novel information theoretic
distinguishers (typically the soft-drop distinguisher) perform the best compared
to known side-channel distinguishers, despite the empty bin situation.