IoT-23

The 'IoT-23' dataset is a benchmark that contains a collection of IoT device traffic data used to evaluate the effectiveness of machine learning models in detecting cyber attacks, particularly zero-day vulnerabilities.

Papers using IoT-23 (6)

NetVAD: Foundation-Model Representation Learning for Identifier-Free Unsupervised Intrusion Detection2026Zero-Day Hunter: A Multi-Layered Machine Learning Framework for Real-Time Detection and Mitigation of Zero-Day Cyber Attacks2026Enhancing security in IoMT using federated TinyGAN for lightweight and accurate malware detection2026Federated Learning-Based Privacy-Preserving Malware Detection Framework Using Multi- Source Cybersecurity Datasets2026Benchmarking Machine Learning Models for IoT Malware Detection under Data Scarcity and Drift2026Revisiting Network Traffic Analysis: Compatible network flows for ML models2025