Cryptographic Key Management Using Neural Network Algorithm

The study's findings present a completely novel method for cryptographic key management that makes use of neural network techniques. Unlike more classic strategies such as the Caesar Cipher and the Vigenère Cipher, our suggested method emphasizes the usage of convolutional neural networks (CNNs) for greater key generation. This method is also unusual in that it employs convolutional neural networks. Therefore, both productivity and safety improve. In terms of scalability and flexibility, recurrent neural networks (RNNs) beat one-time pads and enigma machines for key distribution. Security and efficiency are raised to a greater extent when generative adversarial networks (GANs), a more advanced kind of rotation scheduling than static rotation techniques, are used. These extensive tests indicate that our suggested technique for creating, distributing, and rotating cryptographic keys reaches outstanding performance levels. These levels were determined by measuring the system's capacity to generate, distribute, and rotate keys. This suggests that the technology has the potential to greatly improve data security in online environments. This research provides a novel and practical approach to cryptographic key management, with significant implications for strengthening cybersecurity and protecting private data.