Towards a CIA compliant RSA hybrid built on an Artificial Neural Network
Promise Magoma, Colin Chibaya · 2021
This study investigates the improvement of the RSA algorithm by incorporating a neural network to learn data integrity and data authenticity towards creating a CIA-compliant hybrid RSA model. This is the first time a neural network has been proposed for improving the RSA model towards CIA compliance. Experimental results indicate that a neural network can learn data integrity and data authenticity in RSA encrypted messages. Data analysis affirmed that the neural network learning can be generalized. A conclusion that the RSA algorithm can be upgraded towards CIA compliance when a neural network is incorporated was arrived at. These findings have implications for the commercial standing of the RSA algorithm as well as for the body of knowledge in the cryptography domain.