A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
A survey on the use of data points in IDS research
Tekijät: Heini Ahde, Sampsa Rauti, Ville Leppänen
Toimittaja: Ana Maria Madureira, Ajith Abraham, Niketa Gandhi, Catarina Silva, Mário Antunes
Konferenssin vakiintunut nimi: International Conference on Soft Computing and Pattern Recognition
Julkaisuvuosi: 2019
Journal: Advances in Intelligent Systems and Computing
Kokoomateoksen nimi: Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018)
Sarjan nimi: Advances in Intelligent Systems and Computing
Vuosikerta: 942
Aloitussivu: 329
Lopetussivu: 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
Verkko-osoite: https://doi.org/10.1007/978-3-030-17065-3_33
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/38923661
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
