## 2 Related Work  
Side-channel analysis is a cryptographic attack technique that involves observing information leaks generated by cryptographic systems during their actual operation to obtain knowledge about cryptographic keys or other sensitive information. Due to the diversity of encryption devices and algorithms, there have been numerous works in the field of side-channel analysis. [PERSON] et al. [17] proposed a practical procedure for electromagnetic analysis attack methods and presented experimental results on smart cards. [PERSON] et al. [18] utilized electromagnetic signal leakage between different individuals to distinguish between different instances of the same encryption device. Experimental results demonstrated the effectiveness of deep residual network structures in electromagnetic signal recognition. [PERSON] et al. [19] conducted a security analysis of cache side-channels. [PERSON]. [20] showcased cross-device deep learning side-channel attacks (X-DeepSCA), which employ traces captured from multiple devices for enhanced training and can compromise different target devices. Authors [PERSON] and [PERSON] [21] abstracted side-channel analysis into a regression problem based on circuit state variations and the principles of specific operations in cryptography, using regression models to improve attack accuracy. In addition to research focused on near-field electromagnetic analysis, [PERSON] et al. [22] utilized far-field electromagnetic radiation signals to launch deep learning-based side-channel attacks on various Bluetooth encryption devices, achieving key recovery in an environment located 15 m away from the target. Machine learning also has a strong foundation in the application of side-channel analysis [23; 24]. In [25], the authors used a supervised classification neural network to transform the key enumeration process into a classification process. This greatly reduces the number of attempts on ineffective key datasets in traditional side-channel analysis methods, and the quantitative prior information obtained through the classification process effectively improves the attack efficiency. [PERSON] et al. [26] explored several machine learning techniques to mount a power analysis attack on EdDSA using Curve25519 as implemented in WolfSSL. In reference [27], several commonly used machine learning methods are introduced in the identification and exploitation stages of side-channel analysis. By adjusting the appropriate parameters and conducting training, machine learning methods outperform ordinary template attacks. [PERSON] et al. [28] employed machine learning and neural network models to successfully detect trojans using electromagnetic information leaked from devices. The application of deep learning techniques in side-channel analysis has shown that deep networks provide a more sophisticated analysis of signals and achieve better attack results compared to template attacks and traditional machine learning [29; 30; 31; 32]. It is crucial to train and test on traces captured from different boards and using diverse implementations of the cryptographic algorithm under attack [33]. In reference [34], a systematic understanding of the contributions of deep learning in side-channel analysis is provided, analyzing the different stages where deep learning can be employed. [PERSON] et al. [35] proposed a neural network architecture composed of an encoder, attention mechanism, and classifier for end-to-end analysis. The constructed network can directly classify traces containing a large number of time samples, avoiding complex and tedious feature extraction steps. [PERSON] and [PERSON] [36] highlighted one advantage of deep learning in side-channel analysis, which is its tolerance for misaligned signals. The feature extraction mechanism in neural networks simplifies the data preprocessing stage, reducing workload while improving efficiency. In reference [37], a side-channel attack method based on LSTM deep learning networks is proposed.