| Title | Two-Stage AES Encryption Method Based on Stochastic Error of a Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Peleshchak, Roman, Lytvyn, Vasyl, Kholodna, Nataliia, Peleshchak, Ivan, Vysotska, Victoria |
| Conference Name | 2022 IEEE 16th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET) |
| Keywords | AES, composability, cryptography, Encryption, Forward Error Encryption, Metrics, Network topology, Neural Network, Neurons, passwords, Prediction algorithms, pubcrawl, resilience, Resiliency, Reverse Neural Network Functional Operator, Topology, Training |
| Abstract | This paper proposes a new two-stage encryption method to increase the cryptographic strength of the AES algorithm, which is based on stochastic error of a neural network. The composite encryption key in AES neural network cryptosystem are the weight matrices of synaptic connections between neurons and the metadata about the architecture of the neural network. The stochastic nature of the prediction error of the neural network provides an ever-changing pair key-ciphertext. Different topologies of the neural networks and the use of various activation functions increase the number of variations of the AES neural network decryption algorithm. The ciphertext is created by the forward propagation process. The encryption result is reversed back to plaintext by the reverse neural network functional operator. |
| DOI | 10.1109/TCSET55632.2022.9766991 |
| Citation Key | peleshchak_two-stage_2022 |
Two-Stage AES Encryption Method Based on Stochastic Error of a Neural Network