Two Stage Anomaly Detection for Network Intrusion Detection

Helmut Neuschmied, Martin Winter, Katharina Hofer-Schmitz, Branka Stojanovic, Ulrike Kleb

2021

Abstract

Network intrusion detection is one of the most import tasks in today’s cyber-security defence applications. In the field of unsupervised learning methods, variants of variational autoencoders promise good results. The fact that these methods are very computationally time-consuming is hardly considered in the literature. Therefore, we propose a new two-stage approach combining a fast preprocessing or filtering method with a variational autoencoder using reconstruction probability. We investigate several types of anomaly detection methods mainly based on autoencoders to select a pre-filtering method and to evaluate the performance of our concept on two well established datasets.

Paper Citation

in Harvard Style

Neuschmied H., Winter M., Hofer-Schmitz K., Stojanovic B. and Kleb U. (2021). Two Stage Anomaly Detection for Network Intrusion Detection.In Proceedings of the 7th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-491-6, pages 450-457. DOI: 10.5220/0010233404500457

in Bibtex Style

@conference{icissp21,
author={Helmut Neuschmied and Martin Winter and Katharina Hofer-Schmitz and Branka Stojanovic and Ulrike Kleb},
title={Two Stage Anomaly Detection for Network Intrusion Detection},
booktitle={Proceedings of the 7th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2021},
pages={450-457},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010233404500457},
isbn={978-989-758-491-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - Two Stage Anomaly Detection for Network Intrusion Detection
SN - 978-989-758-491-6
AU - Neuschmied H.
AU - Winter M.
AU - Hofer-Schmitz K.
AU - Stojanovic B.
AU - Kleb U.
PY - 2021
SP - 450
EP - 457
DO - 10.5220/0010233404500457