Identification Of Cryptosystem Based on Deep Neural Network

In network security, deep learning plays a particularly crucial role, where deep learning-based neural networks contribute to data security and accelerate analytical processes. This paper investigates cryptosystem identification using CNNs through controlled experiments and systematic analysis, including control-variable approaches, and the encryption algorithms, parameters and modes of operation are comprehensively explored to evaluate cryptosystems through the construction and innovation of feature engineering and model structures, as well as the continuous adjustment of optimization parameters. Comprehensive experiments reveal that the proposed methodology achieves identification accuracy between 25% and 65% under varying cryptographic configurations, demonstrating its adaptability and effectiveness in practical scenarios such as cryptographic identification, and provides strong support for the wider application of deep learning models in the field of network security.