GitHub

byessilyurt/network-intrusion-detection

A complete machine learning system for detecting volumetric network attacks (DoS/DDoS) using One-Class SVM with SHAP explainability. Includes REST API, interactive dashboard, and Docker deployment.
IEEE

Aircraft Maneuver Intelligent Recognition: An End-to-end Regularized Autoencoder and SVM Approach

Abstract: Aircraft maneuver recognition is a key issue in unmanned aerial vehicle (UAV) intelligent air combat. Aiming at inefficiency of high-dimensional time-series maneuver data analysis and low ...
IEEE

Securing Academic Library VPN Access with AI-Powered Anomaly Detection Models

Abstract: This study explores AI-powered anomaly detection to secure academic digital library access via Virtual Private Networks (VPNs). A three-model framework One-Class SVM, Isolation Forest, and ...
GitHub

feitonyliu/Casting-Products-Defects-Detection

This machine learning project uses anomaly detection models to detect the submersible pump impeller casting defects through images. Casting is a manufacturing process in which a liquid material is ...
devdiscourse

Fast and Accurate Water Leak Detection in Distribution Networks Using Deep Anomaly Models

Researchers from the Politecnico di Milano, Italy, examine one of the most persistent problems facing urban infrastructure systems: undetected water leaks in water distribution networks (WDNs). Framed ...
Scientific Research Publishing

Al-Qatf, M., Lasheng, Y., Al-Habib, M. and Al-Sabahi, K. (2018) Deep Learning Approach Combining Sparse Autoencoder with SVM for Network Intrusion Detection. IEEE Access, 6 ...

ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...