NetFlow Datasets for Machine Learning-based Network Intrusion Detection Systems

TL;DR

This paper introduces NetFlow feature datasets derived from four benchmark NIDS datasets to facilitate consistent evaluation of ML-based intrusion detection systems across different data sources.

Contribution

It provides publicly available NetFlow datasets from multiple benchmarks, enabling more reliable and comparable ML model evaluations for network intrusion detection.

Findings

NetFlow features yield similar binary classification results across datasets.

Multi-class classification performance is lower with NetFlow features compared to original features.

NetFlow datasets are easier to extract and publicly available for research.

Abstract

Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have proven to become a reliable intelligence tool to protect networks against cyberattacks. Network data features has a great impact on the performances of ML-based NIDSs. However, evaluating ML models often are not reliable, as each ML-enabled NIDS is trained and validated using different data features that may do not contain security events. Therefore, a common ground feature set from multiple datasets is required to evaluate an ML model's detection accuracy and its ability to generalise across datasets. This paper presents NetFlow features from four benchmark NIDS datasets known as UNSW-NB15, BoT-IoT, ToN-IoT, and CSE-CIC-IDS2018 using their publicly available packet capture files. In a real-world scenario, NetFlow features are relatively easier to extract from network traffic compared to the complex features used in the original datasets, as they are usually extracted from packet headers. The generated Netflow datasets have been labelled for solving binary- and multiclass-based learning challenges. Preliminary results indicate that NetFlow features lead to similar binary-class results and lower multi-class classification results amongst the four datasets compared to their respective original features datasets. The NetFlow datasets are named NF-UNSW-NB15, NF-BoT-IoT, NF-ToN-IoT, NF-CSE-CIC-IDS2018 and NF-UQ-NIDS are published at http://staff.itee.uq.edu.au/marius/NIDS_datasets/ for research purposes.