ZYELL-NCTU NetTraffic-1.0: A Large-Scale Dataset for Real-World Network Anomaly Detection

TL;DR

This paper introduces ZYELL-NCTU NetTraffic-1.0, a large-scale, real-world network traffic dataset collected from firewalls, aimed at improving anomaly detection in modern network security research.

Contribution

The paper presents a new, large-scale, real-world network traffic dataset that addresses limitations of existing datasets for anomaly detection.

Findings

Provides a comprehensive real-world dataset for network anomaly detection

Facilitates development of more effective IDS models

Addresses data aging and anonymization issues in existing datasets

Abstract

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 However, existing network anomaly datasets are out of date (i.e., being collected many years ago) or IP-anonymized, making the data characteristics differ from today's network.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.