Adaptive and Proximal Federated Optimization for Transferable Network Intrusion Detection

Network Intrusion Detection Systems NIDS based on deep learning often fail to detect attack classes that are absent from training data, limiting their robustness against zero-day threats. This property called transferability across attack classes, remains underexplored especially in federated...

Detecting Cybersecurity Threats by Integrating Explainable AI with SHAP Interpretability and Strategic Data Sampling

The critical need for transparent and trustworthy machine learning in cybersecurity operations drives the development of this integrated Explainable AI XAI framework. Our methodology addresses three fundamental challenges in deploying AI for threat detection: handling massive datasets through...