SEDL: A Symmetric Encryption Method Based on Deep Learning

Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel general symmetry-key encryption method based on deep learning called SEDL, where the secret key includes hyperparameters in deep learning model and the core step of encryption is processing input data with weights trained under hyperparameters. Firstly, both communication parties establish a weight vector table by training a deep learning model on the constructed synthetic training sets according to specified hyperparameters. Then, a self-update codebook is constructed on the weight vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing the corresponding value on the codebook to obtain ciphertext or plaintext, respectively. Results of experiments and relevant analyses show that SEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks, meanwhile, it does not degrade the efficiency of communications.