UNR-IDD: Intrusion Detection Dataset using Network Port Statistics

With the expanded applications of modern-day networking, network infrastructures are at risk from cyber attacks and intrusions. Multiple datasets have been proposed in the 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 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. Using different ML algorithms, we provide a benchmark to show efficient performance for both binary and multi-class classification tasks. 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.