Faisal Saeed

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...