Enhancing Cybersecurity Through Comparative Analysis of Deep Learning Models for Anomaly Detection

Kateřina Macková, Dominik Benk, Martin Šrotýř

2024

Abstract

With the increasing complexity of cyber attacks, traditional methods for anomaly detection in cybersecurity are insufficient, leading to the necessity of integrating deep learning and neural network approaches. This paper presents a comparative analysis of the most powerful deep learning methods for such anomaly detection. We analysed existing datasets for syslog and dataflow, compared several preprocessing methods and identified their strengths and weaknesses. Additionally, we trained and evaluated several deep learning models to provide a comprehensive overview of the current state-of-the-art in cybersecurity. The CNN model achieves excellent results, with 0.999 supervised and 0.938 semi-supervised F1-score in syslog anomaly detection on the BGL dataset and 0.985 F1-score in dataflow anomaly detection on the NIDS dataset. This research contributes to the field of cybersecurity by aiding researchers and practitioners in selecting effective deep-learning models for robust real-life anomaly detection systems. Our findings highlight the reusability of these models in real-life systems.

Paper Citation

in Harvard Style

Macková K., Benk D. and Šrotýř M. (2024). Enhancing Cybersecurity Through Comparative Analysis of Deep Learning Models for Anomaly Detection. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 682-690. DOI: 10.5220/0012312800003648

in Bibtex Style

@conference{icissp24,
author={Kateřina Macková and Dominik Benk and Martin Šrotýř},
title={Enhancing Cybersecurity Through Comparative Analysis of Deep Learning Models for Anomaly Detection},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2024},
pages={682-690},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012312800003648},
isbn={978-989-758-683-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - Enhancing Cybersecurity Through Comparative Analysis of Deep Learning Models for Anomaly Detection
SN - 978-989-758-683-5
AU - Macková K.
AU - Benk D.
AU - Šrotýř M.
PY - 2024
SP - 682
EP - 690
DO - 10.5220/0012312800003648
PB - SciTePress