Hybrid machine learning-driven cyberattack prevention for suspicious IP reputation validation in geographically isolated VPN-tunnelled IoT networks

In this research, we introduce a hybrid supervised machine learning (ML) model to improve IP reputation (IPR) verification in geographically isolated Internet of Things (IoT) subnetworks. In such dynamic and resource-limited environments, traditional IPR systems based on static blacklists and single tagged rules do not work. Using Deep Learning and Random Forest in conjunction, it learns from newfound threats and improves our IPR accuracy by leveraging feature engineering, real-time data handling, and context awareness, i.e., location and device behaviour. It uses federated learning so that data privacy is preserved while performance remains high for weak-network or bandwidth-constrained environments. It urges stress on real-time threat tracking to prevent delay that could turn out to be a huge security risk. Making future improvements with an edge computing + privacy-preserving ML combination to refine the model some more while maintaining 98% accuracy. As a robust solution to protect geographically isolated IoT networks and as a foundation for future adaptive security systems, this research points out supervised ML.