Neyman’s Smoothness Test: A Trade-Off Between Moment-Based and Distribution-Based Leakage Detections

Leakage detection tests have become an indispensable tool for testing implementations featuring side channel countermeasures such as masking. Whilst moment-based techniques such as the Welch’s $t$ -test are universally powerful if there is leakage in a central moment, they naturally fail if this is not the case. Distribution-based techniques such as the $\chi ^{2}$ -test then come to the rescue, but they have shown not to be robust with regards to noise. In this paper, we propose a novel leakage detection technique based on Neyman’s smoothness test. We find that our new test is robust with respect to noise (similar to the merit of Welch’s $t$ -test), and can pick up on leakage that is not located in central moments (similar to the merit of the $\chi ^{2}$ -test). We also find that there is a sweet-spot where Neyman’s test outperforms both the $t$ -test and the $\chi ^{2}$ -test. Realistic measurements confirm that such a sweet-spot is relevant in practice for detecting implementation flaws.