A4 Refereed article in a conference publication
A survey on the use of data points in IDS research
Authors: Heini Ahde, Sampsa Rauti, Ville Leppänen
Editors: Ana Maria Madureira, Ajith Abraham, Niketa Gandhi, Catarina Silva, Mário Antunes
Conference name: International Conference on Soft Computing and Pattern Recognition
Publication year: 2019
Journal: Advances in Intelligent Systems and Computing
Book title : Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018)
Series title: Advances in Intelligent Systems and Computing
Volume: 942
First page : 329
Last page: 337
ISBN: 978-3-030-17064-6
eISBN: 978-3-030-17065-3
DOI: https://doi.org/10.1007/978-3-030-17065-3_33(external)
Web address : https://doi.org/10.1007/978-3-030-17065-3_33(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/38923661(external)
In today's diverse cyber threat landscape, anomaly-based intrusion detection systems that learn the normal behavior of a system and have the ability to detect previously unknown attacks are needed. However, the data gathered by the intrusion detection system is useless if we do not form reasonable data points for machine learning methods to work, based on the collected data sets. In this paper, we present a survey on data points used in previous research in the context of anomaly-based IDS research. We also introduce a novel categorization of the features used to form these data points.
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