Description
The success of horizontal side-channel attacks heavily depends on the quality of the traces as well as the correct extraction of interest areas, which are expected to contain relevant leakages. If former is insufficient, this will consequently degrade the identification capability of potential leakage candidates and often render attacks inapplicable. This work assess the relevance of neural networks in the unsupervised context of horizontal attacks to mitigate noise artefacts from the input signal by proposing two methods with alternative training objectives. Their application results in enhanced traces quality and better exploitability using clustering-based horizontal attacks.