UNSW-NB15

The 'UNSW-NB-15' dataset is a benchmark that contains network traffic data used to evaluate intrusion detection systems, particularly in the context of identifying various types of cyber attacks.

Papers using UNSW-NB15 (13)

Adaptive Client Selection in Federated Learning: A Network Anomaly Detection Use Case2025 · 8 citesNetwork Anomaly Detection in Distributed Edge Computing Infrastructure2025 · 3 citesTowards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks2025 · 1 citesEfficient Client Selection in Federated Learning2025 · 1 citesFederated Naive Bayes with Real Mixture of Gaussians and Institutional Governance Regularization for Network Intrusion Detection2026Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBT2026Zero-Trust Agentic Federated Learning for Secure IIoT Defense Systems2025Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network2025Reducing Communication Overhead in Federated Learning for Network Anomaly Detection with Adaptive Client Selection2025MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network2025Federated PCA on Grassmann Manifold for IoT Anomaly Detection2024 · 33 citesNetwork Anomaly Detection Using Federated Learning2023 · 1 citesEnhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning2023