Fuzzy Logic for Cybersecurity: Intrusion Detection and Privacy Preservation with Synthetic Data

Marina Soledad Iantorno, Khalil Beladda

2025

Abstract

This research explores the use of fuzzy logic in intrusion detection systems (IDS) aiming to improve cybersecurity threat detection. Conventional machine learning models, like Decision Trees and Support Vector Machines, are evaluated against a fuzzy logic model that employs triangle and parallelogram-shaped membership functions to address the uncertainty in network traffic. The fuzzy logic system presented good performance, achieving greater accuracy, precision, and F1-scores than conventional models, particularly when using real network traffic data. Synthetic data produced by Wasserstein Generative Adversarial Networks (WGANs) was also used to evaluate the model's robustness and guarantee privacy protection in future studies. The relevance of this approach lies in its ability to provide more comprehensive threat detection, helping organizations safeguard their systems in environments where strict, rule-based models may fall short. The findings indicate that the fuzzy logic methodology is effective, even when applied to synthetic data, demonstrating its feasible choice for intrusion detection in sensitive contexts. Subsequent research will investigate the incorporation of deep learning methodologies and the modification of the model for distributed systems, focusing on scalability and real-time threat identification.

Paper Citation

in Harvard Style

Iantorno M. and Beladda K. (2025). Fuzzy Logic for Cybersecurity: Intrusion Detection and Privacy Preservation with Synthetic Data. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 376-382. DOI: 10.5220/0013137300003890

in Bibtex Style

@conference{icaart25,
author={Marina Iantorno and Khalil Beladda},
title={Fuzzy Logic for Cybersecurity: Intrusion Detection and Privacy Preservation with Synthetic Data},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={376-382},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013137300003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Fuzzy Logic for Cybersecurity: Intrusion Detection and Privacy Preservation with Synthetic Data
SN - 978-989-758-737-5
AU - Iantorno M.
AU - Beladda K.
PY - 2025
SP - 376
EP - 382
DO - 10.5220/0013137300003890
PB - SciTePress