Security and Privacy Preservation of Multimedia Objects Over the Internet of Multimedia of Things (IoMT)

The surge in technological advancements has accentuated the ubiquity of Internet of Things (IoT) networks and amplified the reliance on data-driven models in diverse domains, notably in healthcare. This convergence of innovations, however, has exposed an array of vulnerabilities and necessitated the development of robust frameworks for security and privacy preservation. In this multifaceted study, we deploy an ensemble of machine learning (ML) and deep learning (DL) models to address network intrusions and scrutinize the application of federated learning in healthcare as a pivotal privacy-preserving technique. We initiated our exploration by employing models like logistic regression, decision trees, random forests, and multi-layer perceptron classifier on the UNSW-NB15 dataset, aiming to discern their effectiveness in detecting and classifying network attacks on IoMT networks. A nuanced methodology integrating random forest and feedforward neural networks is also presented, offering enhanced insights into attack categorization and feature importance. The findings underscore the potential of advanced ML models and privacy-preserving techniques in fostering a secure, resilient, and ethical digital ecosystem, paving the way for future research and development in these pivotal domains. The amalgamation of these insights not only contributes to the advancements in network security and data privacy but also lays down a foundational framework for the responsible and innovative evolution of technology in the digital era.