Generating Realistic Cyber Security Datasets for IoT Networks with Diverse Complex Network Properties

Fouad Al Tfaily, Fouad Al Tfaily, Zakariya Ghalmane, Mortada Termos, Mortada Termos, Mohamed-el-Amine Brahmia, Ali Jaber, Mourad Zghal

2025

Abstract

In the cybersecurity community, finding suitable datasets for evaluating Intrusion Detection Systems (IDS) is a challenge, particularly due to limited diversity in complex network properties. This paper proposes a dual-purpose approach that generates diverse datasets while producing efficient, compact versions that maintain detection accuracy. Our approach employs three techniques - community mixing modification, centrality-based modification, and time-based modification - each targeting specific network property adjustments while achieving significant dataset size reductions (up to 81.5%). Our approach is validated on real-world datasets, including NF-UQ-NIDS, CCD-INID-V1, and TON-IoT, demonstrating its ability to generate realistic datasets while preserving network properties, attack patterns, and structural integrity. The generated datasets exhibit diverse complex network properties, making them particularly useful for IDS technique evaluation that incorporates complex network measures. The reduced size and preserved accuracy (96.4%) make these datasets especially valuable for resource-constrained environments. Moreover, our approach facilitates the construction of homogeneous datasets required for federated learning situations where data distribution similarity across clients is essential. This contribution helps address both dataset scarcity and computational efficiency challenges while ensuring that the generated datasets retain the characteristics of real-world network traffic.

Paper Citation

in Harvard Style

Al Tfaily F., Ghalmane Z., Termos M., Brahmia M., Jaber A. and Zghal M. (2025). Generating Realistic Cyber Security Datasets for IoT Networks with Diverse Complex Network Properties. In Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS; ISBN 978-989-758-750-4, SciTePress, pages 321-328. DOI: 10.5220/0013359000003944

in Bibtex Style

@conference{iotbds25,
author={Fouad Al Tfaily and Zakariya Ghalmane and Mortada Termos and Mohamed-el-Amine Brahmia and Ali Jaber and Mourad Zghal},
title={Generating Realistic Cyber Security Datasets for IoT Networks with Diverse Complex Network Properties},
booktitle={Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS},
year={2025},
pages={321-328},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013359000003944},
isbn={978-989-758-750-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS
TI - Generating Realistic Cyber Security Datasets for IoT Networks with Diverse Complex Network Properties
SN - 978-989-758-750-4
AU - Al Tfaily F.
AU - Ghalmane Z.
AU - Termos M.
AU - Brahmia M.
AU - Jaber A.
AU - Zghal M.
PY - 2025
SP - 321
EP - 328
DO - 10.5220/0013359000003944
PB - SciTePress