Enhancing Data Security through Machine Learning-based Key Generation and Encryption
Received: 2 March 2024 | Revised: 26 March 2024 | Accepted: 3 April 2024 | Online: 1 June 2024
Corresponding author: Abhishek Saini
Abstract
In an era marked by growing concerns about data security and privacy, the need for robust encryption techniques has become a matter of paramount importance. The primary goal of this study is to protect sensitive information during transmission while ensuring efficient and reliable decryption at the receiver's side. To generate robust and unique cryptographic keys, the proposed approach trains an autoencoder neural network based on hashing and optionally generated prime numbers in the MNIST dataset. The key serves as the foundation for secure communication. An additional security layer to the cryptographic algorithm passing through the first ciphertext, was employed utilizing the XORed and Blum-Blum-Shub (BBS) generators to make the system resistant to various types of attacks. This approach offers a robust and innovative solution for secure data transmission, combining the strengths of autoencoder-based key generation and cryptographic encryption. Its effectiveness is demonstrated through testing and simulations.
Keywords:
AES, autoencoder, DES, ElGamal, key generation, neural cryptography, RSADownloads
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