A Neural Network Enhanced RSA Model Towards a Confidentiality-Integrity-Authenticity Compliant Hybrid

Abstract

Data security is an important aspect in the field of data science where data collection, analysis, interpretation, and sharing are a primary goal. To prevent unauthorized access to data, creative methods to securing data are sought. Cryptography is about the development of algorithms with which to hide data. The three key objectives of cryptography are to achieve data confidentiality (C), data integrity (I), and data authenticity (A). Algorithms that can achieve all these three objectives at once are said to be CIA compliant. However, there are barely any algorithms out there that can satisfy these three objectives in one goal. However, CIA-compliant cryptosystems are, to the best of our knowledge, rare. The RSA algorithm is a compelling cryptosystem that was mainly designed to achieve data confidentiality. It demonstrates attractive properties for improvement towards CIA compliancy. Some research has tried to upgrade the RSA algorithm by combining it with the DH model or the El Gamal model. However, still, the outcome would either be CI or CA compliant, leaving out one of the three objectives. 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. To the best of our knowledge, 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 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.