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

Marta Catillo, Antonio Pecchia, Umberto Villano

2025

Abstract

Network intrusion detection systems (NIDS) play a key role for cybersecurity. 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, DoS 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.

Paper Citation

in Harvard Style

Catillo M., Pecchia A. and Villano U. (2025). USB-IDS-TC: A Flow-Based Intrusion Detection Dataset of DoS Attacks in Different Network Scenarios. In Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-735-1, SciTePress, pages 302-309. DOI: 10.5220/0013248600003899

in Bibtex Style

@conference{icissp25,
author={Marta Catillo and Antonio Pecchia and Umberto Villano},
title={USB-IDS-TC: A Flow-Based Intrusion Detection Dataset of DoS Attacks in Different Network Scenarios},
booktitle={Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2025},
pages={302-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013248600003899},
isbn={978-989-758-735-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - USB-IDS-TC: A Flow-Based Intrusion Detection Dataset of DoS Attacks in Different Network Scenarios
SN - 978-989-758-735-1
AU - Catillo M.
AU - Pecchia A.
AU - Villano U.
PY - 2025
SP - 302
EP - 309
DO - 10.5220/0013248600003899
PB - SciTePress