Real-time network anomaly detection system using machine learning for cybersecurity monitoring and threat detection

A hybrid IDS for aircraft is proposed using Random Forests, Isolation Forests, YARA rules, and import hashing within a zero-trust architecture. Evaluated in a virtualized multi-zone testbed, it achieves high accuracy across six aviation datasets with low resource use, enabling practical onboard cybersecurity.

A context-aware intrusion detection framework extending Kitsune with adaptive risk assessment, explainable AI, and counterfactual explanations for network security.

A dashboard which tracks the alarms and alerts, statuses and metrics of the WLCG/OSG sites

AI-Powered Autonomous Network Defense Appliance for Real-Time Network Monitoring, Threat Detection, and Automated Response using Machine Learning.