<p>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 triplet network’s embedded representations are refined using an SVM classifier, which significantly enhances classification accuracy and robustness in high-dimensional spaces. This study proposes an enhanced DLATA framework that integrates multi-domain feature fusion using wavelet transform (WT) and support vector machine (SVM) techniques to address these challenges. This approach is referred to as MD-DLATA. Specifically, Daubechies 4 (db4) is chosen as the mother wavelet function for multi-resolution feature extraction on side-channel data, allowing the triplet network to learn within a denoised, high-quality feature space. The triplet network’s embedded representations are further refined using an SVM classifier with a radial basis function (RBF) kernel. Experiments on the publicly available ASCAD dataset confirm that our method exhibits strong resistance to noise. Across various noise levels, our method reduces the number of traces required to achieve Guessing Entropy (GE) &lt; 1 by 17-29% compared to the original method.</p>