The exponential growth of the Internet of Things (IoT) has revolutionized connectivity across various domains but has also introduced significant security vulnerabilities due to the constrained nature of IoT devices. Traditional cryptographic techniques, though robust in standard computing environments, often fall short in resource-limited IoT settings due to their high computational demands. To address this challenge, the present study conducts a comparative analysis between conventional cryptographic methods (e.g., AES, RSA, ECC) and Artificial Neural Network (ANN)-based cryptographic systems in the context of IoT security.

The study aims to evaluate the effectiveness, efficiency, and adaptability of ANN-integrated cryptographic models compared to traditional algorithms under different IoT use-case scenarios. Using a simulation-based approach, various IoT environments such as smart homes and healthcare monitoring systems were emulated. Deep learning models, including feed forward and recurrent neural networks, were trained to optimize or support encryption tasks. Tools like Tensor Flow, NS3, and Python were used to implement and evaluate the cryptographic frameworks.

The results indicate that ANN-based systems exhibit enhanced adaptability, lower latency, and improved energy efficiency, particularly in real-time and low-power scenarios, without significantly compromising security. This study contributes to the evolving field of secure AI-driven IoT systems by offering empirical evidence on the potential of deep learning models to complement or enhance existing cryptographic protocols.