The deep learning-assisted template attack (DLATA) is an advanced side-channel attack (SCA) technique that employs a triplet network to embed side-channel features efficiently within a template attack framework. This method utilizes a triplet network to efficiently embed input data into a template attack (TA) in a single training iteration. Although the triplet network is highly effective in feature extraction, its performance deteriorates significantly in high-noise environments, thereby highlighting the need for further improvements. The trip...
