## Cryptology ePrint Archive: Report 2019/1478

Leakage Detection with Kolmogorov-Smirnov Test

Xinping Zhou and Kexin Qiao and Changhai Ou

Abstract: Leakage detection seeking the evidence of sensitive data dependencies in the side-channel traces instead of trying to recover the sensitive data directly under the enormous efforts with numerous leakage models and state-of-the-art distinguishers can provide a fast preliminary security assessment on the cryptographic devices for designers and evaluators. Therefore, it is a popular topic in recent side-channel research of which the Welch's $t$-test-based Test Vector Leakage Assessment (TVLA) methodology is the most widely used one. However, the TVLA is not always the best option under all kinds of conditions (as we can see in the latter section of this paper). Kolmogorov-Smirnov test is a well-known nonparametric method for statistical analysis to determine whether the samples are from the same distribution by analyzing the cumulative distribution. It has been proposed into side-channel analysis as a successful distinguisher. This paper proposes---to our knowledge, for the first time---Kolmogorov-Smirnov test as a new method for leakage detection. Besides, we propose two implementations to speed up the KS leakage detection procedure. Experimental results on simulated leakage with various parameters and the practical traces verify that KS is an effective and robust leakage detection tool and the comprehensive comparison with TVLA shows that KS-based leakage detection can be a right-hand supplement to TVLA when performing the side-channel assessment.

Category / Keywords: implementation / Side Channel Analysis, Kolmogorov-Smirnov Test, Leakage Detection, Cumulative Distribution Function, Histogram