ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection
This provides a new dataset for researchers in network security to improve anomaly detection in intrusion detection systems, though it is incremental as it focuses on data rather than methods.
The authors tackled the problem of outdated or anonymized network anomaly datasets by introducing ZYELL-NCTU NetTraffic-1.0, a large-scale, real-world dataset collected from firewall outputs to advance network security research.
Network security has been an active research topic for long. One critical issue is improving the anomaly detection capability of intrusion detection systems (IDSs), such as firewalls. However, existing network anomaly datasets are out of date (i.e., being collected many years ago) or IP-anonymized, making the data characteristics differ from today's network. Therefore, this work introduces a new, large-scale, and real-world dataset, ZYELL-NCTU NetTraffic-1.0, which is collected from the raw output of firewalls in a real network, with the objective to advance the development of network security researches.