  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 text encryption method based
on deep learning called TEDL, where the secret key includes hyperparameters in
deep learning model and the core step of encryption is transforming input data
into weights trained under hyperparameters. Firstly, both communication parties
establish a word vector table by training a deep learning model according to
specified hyperparameters. Then, a self-update codebook is constructed on the
word vector table with the SHA-256 function and other tricks. When
communication starts, encryption and decryption are equivalent to indexing and
inverted indexing on the codebook, respectively, thus achieving the
transformation between plaintext and ciphertext. Results of experiments and
relevant analyses show that TEDL 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 while not degrade the communication efficiency.
