A Secret Key Classification Framework of Symmetric Encryption Algorithm Based on Deep Transfer Learning

• Published in: Applied sciences Vol. 13; no. 21; p. 12025
• Main Authors: Cui, Xiaotong, Zhang, Hongxin, Fang, Xing, Wang, Yuanzhen, Wang, Danzhi, Fan, Fan, Shu, Lei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: AES; Algorithms; Classification; Cryptography; Datasets; Deep learning; Design; Efficiency; Electromagnetism; Investment analysis; Machine learning; Neural networks; Radiation; side-channel attack; symmetrical decision; transfer learning; China
• DOI: 10.3390/app132112025
• ISSN: 2076-3417

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.