A Modified RSA Algorithm Using Machine Learning and DNA Cryptography Techniques

Abstract

RSA (Rivest Shamir Adleman) is an asymmetric algorithm used for encryption and decryption of data to ensure security through a communication channel. RSA algorithm is preferred over symmetric algorithms as it employs asymmetric keys for encryption and decryption. In this work, RSA optimization is attempted at three stages: key generation, data encryption, and data decryption. Key generation is implemented using the Gradient descent algorithm and Newton's method. Prime numbers required in key generation, data encryption, and data decryption are generated using GPU computations using OpenCL and CPU as the host providing a heterogeneous platform. The second and third stages namely data encryption and data decryption are implemented using the DNA cryptographic techniques and gray coding. The modified RSA designed as depicted above uses a low-key length of 8 bits and is validated for performance through avalanche effect, encryption simulation time, and decryption simulation time. The modified RSA algorithm is further verified by analyzing the chosen ciphertext attack time mathematically for various key lengths from 8 to 2048. The increase in attack time is around 93% for a key length of 8 bits and 78% for 15 bits for a key length of 2048 it increases by 100%.