NeuroChaosCrypt: Revolutionizing Chaotic-Based Cryptosystem With Artificial Neural Networks—A Comparison With Traditional Cryptosystems

Abstract

Encryption systems play a vital role in the transfer of sensitive data, and the integration of chaotic systems into this domain has garnered significant attention. However, these systems often grapple with complexity and insufficient security, posing challenges for real-world implementation. Researchers introduced the synchronization techniques to fix these problems, which means making sure that the chaotic systems in bothe the transmitter and receiver systems behave in a way that can be understood so that accurate signal recovery can happen. Chaotic system synchronisation presents challenges and security risks besides the limited of the encryption keys make them subjected to attacks. Because of their advantages over traditional chaotic systems in terms of flexibility, adaptability, and computational efficiency, artificial neural networks, or ANNs, are being used more and more to study chaotic systems. This paper presents NeuroChaosCrypt, a novel cryptographic framework employing unique methodologies for secure data transmission. It utilizes an Artificial Neural Network (ANN)-based chaotic system at both transmitter and receiver, eliminating the need for synchronization. A comprehensive case study, including audio signal transmission, underscores NeuroChaosCrypt's efficacy. Comparison with a traditional encryption system integrating a Linear Quadratic Regulator (LQR) controller reveals comparable security levels, correlation coefficient (cc), Signal-to-Noise Ratio (SNR), Peak-to-Root Mean Square Distortion (PRD), and encryption time. NeuroChaosCrypt, enhanced by ANNs, excels in decryption speed, key-space coverage, and hardware implementation using field-programmable gate arrays (FPGAs). This methodology achieves a higher maximum frequency while requiring fewer logic units. The comparison offers valuable insights into audio encryption methods, aiding informed decision-making for selecting the most suitable solution based on specific application requirements. Finally, we introduce an application of the proposed NeuroChaosCrypt for image encryption to ensure that thr study can exploit other data types for broader applicability.