USB-IDS-TC: A Flow-Based Intrusion Detection Dataset of DoS Attacks in Different Network Scenarios

Network intrusion detection systems (NIDS) play a key role for cyber security. Most of the times, NIDS are built on machine learning/deep learning (ML/DL) models that are trained and tested on public intrusion detection datasets. This paper presents the novel USB-IDS-TC dataset, conceived to explore the dependence of ML/DL-based NIDS on the network used to collect the training traffic data. In this new publicly-available dataset, Do S attacks have been conducted in different network scenarios, in the belief that the network has a non-negligible effect on the detection capability of the NIDS as indicated by our initial analysis. Differently from existing datasets that collect the data in a single scenario, USB-IDS-TC allows studying the dependence of the attacks, traffic features and ML/DL models on the network, in order to strive for generalizable and widely-applicable NIDS.