Cryptanalysis in Transfer learning using Advanced Optimal Approach Based on Deep Neural Network

Abstract

Cryptanalysis is essential for evaluating the robustness of modern encryption algorithms such as the Advanced Encryption Standard (AES) against various attacks. However, traditional cryptanalysis methods are often time-consuming, require deep domain expertise, and struggle to scale effectively when analysing high-dimensional data such as cipher texts and side-channel emissions. Moreover, the lack of standardized analysis techniques poses challenges in identifying subtle vulnerabilities and anomalies within AES implementations. To address these limitations, this study proposes a novel cryptanalysis framework based on transfer learning, which leverages pre-trained deep learning models from diverse domains to automatically extract and analyse meaningful features from side-channel data and AES-related cryptographic elements. This Transfer Learning-based Cryptanalysis Framework (TLCF) significantly reduces manual feature engineering and improves the detection of information leakage, structural anomalies, and potential attack vectors in AES systems. While steganography and cryptanalysis target distinct threat surfaces hidden communication and key recovery respectively both benefit from transfer learning’s ability to extract robust, transferable features from complex input domains. This unified approach enables the application of deep feature learning across heterogeneous security tasks. Furthermore, knowledge distilled from related ciphers such as DES and Serpent is incorporated to enhance generalization and robustness across different cryptographic settings. Experimental evaluations demonstrate that the proposed approach achieves high accuracy in vulnerability detection and outperforms traditional methods, especially when analysing the impact of fault injections on AES implementations. By integrating transfer learning into the cryptanalysis pipeline, this work advances the automation, efficiency, and precision of evaluating encryption schemes, contributing to a deeper understanding of cryptographic security.