With the expanded applications of modern-day networking, network infrastructures are at risk from cyber attacks and intrusions. Multiple datasets have been proposed in literature that can be used to create Machine Learning (ML) based Network Intrusion Detection Systems (NIDS). However, many of these datasets suffer from sub-optimal performance and do not adequately represent all types of intrusions in an effective manner. Another problem with these datasets is the low accuracy of tail classes. To address these issues, in this paper, we propose the University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD) that provides researchers with a wider range of samples and scenarios. The proposed dataset utilizes network port statistics for more fine-grained control and analysis of intrusions. We provide a benchmark to show efficient performance for both binary and multi-class classification tasks using different ML algorithms. The paper further explains the intrusion detection activities rather than providing a generic black-box output of the ML algorithms. In comparison with the other established NIDS datasets, we obtain better performance with an Fµ score of 94% and a minimum F score of 86%. This performance can be credited to prioritizing high scoring average and minimum F-Measure scores for modeled intrusions.

This manuscript has been accepted for publication in 2023 IEEE Consumer Communications and Networking Conference.