Deep Learning Leakage Assessment

In this work, we explore the topic of Machine Learning (ML) in the area of Leakage Assessment (LA), a subsection of the field of Side-Channel Analysis (SCA). We focus on Deep Learning Leakage Assessment (DL-LA), as proposed by Wegener et al., and its relation to the established Test Vector Leakage Assessment (TVLA). We will do this in the context of the Advanced Encryption Standard (AES). To explore the relation of DL-LA to its SCA counterparts, we will use the ASCAD database from ANSSI, among others.We find that DL-LA techniques are more sensitive than TVLA methods on AES-128 in scenarios without protection and with Window jitter, Gaussian noise, and first-order boolean masking. When detecting higher-moment order leakages, DL-LA techniques are barely influenced by anomalous traces. In contrast, results of TVLA with a higher statistical moment order t-test are slashed when only one trace is misaligned. We propose a novel way of performing DL-LA to remove one of the requirements of the DL-LA method of Wegener et al., namely that the attack traces need to be balanced. This DL-LA method is a multi-class leakage assessment method. It finds its similarities in the Sum-Of-Squared T-values (SOST) leakage detection method. We show it to be generally more sensitive than the DL-LA method described by Wegener et al