###### Abstract  
The leakage signals, including electromagnetic, energy, time, and temperature, generated during the operation of password devices contain highly correlated key information, which leads to security vulnerabilities. In traditional encryption algorithms, the length of the key greatly affects the upper limit of its security against cracking. Regarding side-channel attacks on long-key algorithms, traditional template attack methods characterize the energy traces using multivariate Gaussian distribution during the template construction phase. The exhaustive key-guessing process is expected to consume a significant amount of time and computational resources. Therefore, to analyze the effectiveness of obtaining key values from the side information of password devices, we propose an innovative attack method based on a divide-and-conquer logical structure, targeting semi-bytes. We construct a collection of key classification submodules with symmetric correlations. By integrating a differential network model for byte-block sets and an end-to-end direct attack method, we form a holistic symmetric decision framework and propose a key classification structure based on deep transfer learning. This structure consists of three main parts: side information data acquisition, analysis of key-value effectiveness, and determination of attack positions. It employs multiple parallel symmetric subnetworks, effectively improving attack efficiency and reducing the key enumeration range. Experimental results show that the optimal attack accuracy of the network model can reach 91%, with an average attack accuracy of 78%. It overcomes overfitting issues under small sample dataset conditions. s +
Footnote †: journal: _sciences_  
0000-0001-919-22000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-000000-0000000-0 implementations. This contributes to ensuring the confidentiality, integrity, and availability of data, as well as protecting personal privacy and sensitive information [2]. Since the introduction of timing attacks [3], side-channel analysis has posed new challenges to the security of cryptographic devices compared to traditional cryptographic analysis. Side-channel information is collected without attempting to break the encryption or authentication mechanisms themselves. Instead, it involves extracting information by analyzing the behavior or characteristics of the system. This makes such attacks difficult to detect and exhibits a dual nature of high flexibility and high threat. Traditional side-channel analysis techniques include correlation power analysis [4], differential power analysis [5], template attacks [6; 7; 8], and others. These methods exploit the statistical characteristics of leaked signal amplitudes in the device and rely on the data being processed in the device to make guesses about the actual key. Among them, template attacks characterize the energy traces using multivariate normal distributions, and the template is constructed based on mean vectors and covariance matrices for key pairs. Such key-scanning enumeration structures face significant probability retrieval demands in long key systems, and the computational power of practical devices cannot support the required attack actions within a reasonable time-frame. Compared to traditional side-channel attack methods, the application of machine learning [9] and deep learning [10; 11] has brought new perspectives in side-channel analysis [12]. However, existing deep learning methods still have limitations in side-channel analysis. Deep learning has been well-established for tasks such as image processing [13; 14] and speech recognition [15; 16], but its application in cryptographic algorithms is relatively limited. Classical handwritten digit classification involves 10 classes, while the analysis of cryptographic keys requires exponentially more classification categories, making existing classification models less suitable.