Fuad A. Ghaleb

A Hybrid Deep Autoencoders and Random Forest Framework for False Data Injection Attack Detection in Industrial Internet of Things Networks

A hybrid detection framework is proposed that integrates a Random Forest classifier with an unsupervised anomaly detection model based on a deep autoencoder combined through a Logistic Regression metaclassifier that addresses the gap in single-model detectors that either rely on fixed decision boundaries that struggle with gradually evolving stealthy FDIA patterns or on anomaly detection that lacks strong discriminative power in separating subtle adversarial deviations from normal operational variability.