| Title | Optimization of Encrypted Communication Length Based on Generative Adversarial Network |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Duan, Xiaowei, Han, Yiliang, Wang, Chao, Ni, Huanhuan |
| Conference Name | 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI) |
| Date Published | jul |
| Keywords | Adaptation models, Collaboration, composability, compositionality, cryptology, Data models, Encryption, Error analysis, generative adversarial networks, loss function, machine learning, Metrics, model optimization, Neural Network, Neural networks, policy governance, pubcrawl, resilience, Resiliency, Training |
| Abstract | With the development of artificial intelligence and cryptography, intelligent cryptography will be the trend of encrypted communications in the future. Abadi designed an encrypted communication model based on a generative adversarial network, which can communicate securely when the adversary knows the ciphertext. The communication party and the adversary fight against each other to continuously improve their own capabilities to achieve a state of secure communication. However, this model can only have a better communication effect under the 16 bits communication length, and cannot adapt to the length of modern encrypted communication. Combine the neural network structure in DCGAN to optimize the neural network of the original model, and at the same time increase the batch normalization process, and optimize the loss function in the original model. Experiments show that under the condition of the maximum 2048-bit communication length, the decryption success rate of communication reaches about 0.97, while ensuring that the adversary's guess error rate is about 0.95, and the training speed is greatly increased to keep it below 5000 steps, ensuring safety and efficiency Communication. |
| DOI | 10.1109/BDAI52447.2021.9515301 |
| Citation Key | duan_optimization_2021 |
Optimization of Encrypted Communication Length Based on Generative Adversarial Network