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arXiv 2003.04038cs.CRcs.CLcs.LG

TEDL: A Text Encryption Method Based on Deep Learning

School of Information Science and Engineering, Southeast University(东南大学信息科学与工程学院);School of Computer Science and Engineering, Southeast University(东南大学计算机科学与工程学院);School of Cyber Science and Engineering, Southeast University(东南大学网络空间安全学院)

Xiang Li, Peng Wang

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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