Information Theoretic Distinguishers for Timing Attacks with Partial Profiles: Solving the Empty Bin Issue

In any side-channel attack, it is desirable to exploit all the available leakage data compute distinguisher’s values. The profiling phase essential obtain an accurate model, yet may not be exhaustive. As a result, information theoretic distinguishers come up on previously unseen data, phenomenon yielding empty bins. A strict application of maximum likelihood method yields distinguisher that even sound. Ignoring bins reestablishes soundness, but seriously limits its performance in terms success rate. purpose this paper remedy situation. research, we propose six different techniques improve distinguishers. We study them thoroughly by applying timing attacks, both with synthetic and real leakages. Namely, compare rate, show their depends amount profiling, can explained bias-variance analysis. result our work there exist use-cases, especially when measurements are noisy, where novel (typically soft-drop distinguisher) perform best compared known distinguishers, despite bin