Security Challenges and Solutions in IoT-Based Image Processing Using Machine Learning Techniques

Abstract

The incorporation of Internet of Things IoT technology in image processing poses major security risks in data integrity, privacy and dynamism in threats. That’s why this work intends to introduce an optimized machine learning-based framework that will improve security in IoT-based image processing. To solve the fundamental security challenges such as unauthorized access, data manipulation and malware penetration, the framework uses convolutional neural networks for anomaly detection, and adaptive encryption and authentication. The model was also examined in different conditions and it predicted a high detection rate and competent processing of conditions accompanied with many IoT devices. The work depicts the loss of privacy in exchange for model performance and explains how the model has high performance even in conditions when privacy is a concern. Based on the findings, the authors indicate that the solution proposed can readily be applied in higher risk areas for security such as smart city and surveillance and healthcare information systems respectively indicating that the proposed solution is potentially scalable for IoT security needs.