| Title | Optimization of Encrypted Communication Model Based on Generative Adversarial Network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Duan, Xiaowei, Han, Yiliang, Wang, Chao, Ni, Huanhuan |
| Conference Name | 2022 International Conference on Blockchain Technology and Information Security (ICBCTIS) |
| Keywords | artificial intelligence, Brain modeling, Computational modeling, convolutional neural network, Error analysis, Generative Adversarial Learning, generative adversarial networks, machine learning algorithms, Metrics, Plaintext leakage, pubcrawl, resilience, Resiliency, Scalability, Stability analysis, Training |
| Abstract | With the progress of cryptography computer science, designing cryptographic algorithms using deep learning is a very innovative research direction. Google Brain designed a communication model using generation adversarial network and explored the encrypted communication algorithm based on machine learning. However, the encrypted communication model it designed lacks quantitative evaluation. When some plaintexts and keys are leaked at the same time, the security of communication cannot be guaranteed. This model is optimized to enhance the security by adjusting the optimizer, modifying the activation function, and increasing batch normalization to improve communication speed of optimization. Experiments were performed on 16 bits and 64 bits plaintexts communication. With plaintext and key leak rate of 0.75, the decryption error rate of the decryptor is 0.01 and the attacker can't guess any valid information about the communication. |
| DOI | 10.1109/ICBCTIS55569.2022.00016 |
| Citation Key | duan_optimization_2022 |
Optimization of Encrypted Communication Model Based on Generative Adversarial Network