Cryptography model design with artificial intelligence and performance evaluation

Abstract

The growth of digital business and communication systems has brought about an increase in risks. As the digital world expands, so does the use of its components. Trillions of transactions occur online every hour, making safety crucial when exchanging data over a public communication channel. Cryptography is one method for providing this safety. While many cryptographic algorithms have been developed using graph theory and conventional mathematics, few articles on cryptography have been published that use artificial neural networks and DNA structure as their basis. The researchers attempted to discover new methods with the latest technology to keep confidential information secure and improve the robustness of the communication system. In this paper, two cryptography algorithms have been presented that are built on the idea of tree parity machines. The input message is converted to binary and fed into the encryption system. The key has been generated dynamically using the ANN technique. The performance of the developed algorithms has been evaluated and analyzed considering the parameters such as execution time, throughput, memory use, and strength. The obtained outcomes were found to be comparatively better than existing algorithms.