<p>Deep learning methods have significantly impact in the side-channel attack (SCA) community. However, the training and verification phases of deep learning-based side-channel attacks (DL-SCA) typically focus on a single byte, which leads to the requirement of training numerous models to recover all partial key bytes. To resolve the problem, this paper proposes the TripM model, triple-keys attack model, which can attack three bytes in a single training session. First, TripM leverages label groups black to learn multiple bytes of leaked information in a single training session, where the label groups refers to divide labels to different groups according to the different attack bytes. The labels of TripM comprise three label groups, each group containing the point-of-interest information of the corresponding key. Second, the architectural design of TripM features two identical convolutional branches, allowing for the application of weight-sharing techniques. Both branches utilize the same weights, reducing the size of the model parameters and accelerating the training process. Finally, the TripM model employs a multithreading technique in the key recovery phase, where three threads concurrently compute the 3-byte Guessing Entropy (GE) value. Experimental results demonstrate that TripM can efficiently process the public ASCAD and TinyPower datasets, with an average of 80 and 89 traces required to recover a key. Average Layer-wise Correlation (AVE-LWC) visualization techniques also illustrate that TripM possesses excellent feature extraction capabilities.</p>