2026-05-31T00:10:57Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/66352022-12-19T09:57:05Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Quintián, Héctor Jove, Esteban Casteleiro-Roca, José-Luis Urda Muñoz, Daniel Arroyo Puente, Ángel Calvo-Rolle, José Luis Herrero Cosío, Álvaro Corchado, Emilio 2022-05-04T13:38:27Z 2022-05-04T13:38:27Z 2022-12 1367-0751 http://hdl.handle.net/10259/6635 10.1093/jigpal/jzac013 1368-9894 Detecting intrusions in large networks is a highly demanding task. In order to reduce the computation demand of analysing every single packet travelling along one of such networks, some years ago flows were proposed as a way of summarizing traffic information. Very few research works have addressed intrusion detection in flows from a visualizations perspective. In order to bridge this gap, the present paper proposes the application of a novel projection method (Beta Hebbian Learning) under this framework. With the aim to validate this method, 8 traffic segments, containing many flows, have been analysed by means of this projection method. The promising results obtained for these segments, extracted from the University of Twente dataset, validate the proposed application. Funding for open access charge: Universidade da Coruña/CISUG. application/pdf eng Oxford University Press Logic Journal of the IGPL. 2022, V. 30, n. 6, p. 1056–1073 https://doi.org/10.1093/jigpal/jzac013 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Intrusion detection Traffic flow Exploratory projection pursuit Visualization Artificial neural networks Unsupervised learning Informática Computer science Advanced Visualization of Intrusions in Flows by Means of Beta-Hebbian Learning info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Logic Journal of the IGPL