Quintián, HéctorJove, EstebanCasteleiro-Roca, José-LuisUrda Muñoz, DanielArroyo, ÁngelCalvo-Rolle, José LuisHerrero, AlvaroCorchado, Emilio2024-07-292024-07-292022-12Héctor Quintián, Esteban Jove, José-Luis Casteleiro-Roca, Daniel Urda, Ángel Arroyo, José Luis Calvo-Rolle, Álvaro Herrero, Emilio Corchado, Advanced Visualization of Intrusions in Flows by Means of Beta-Hebbian Learning, Logic Journal of the IGPL, Volume 30, Issue 6, December 2022, Pages 1056–1073, https://doi.org/10.1093/jigpal/jzac0131368-9894http://hdl.handle.net/2183/38290Funding for open access charge: Universidade da Coruña/CISUG.[Abstract] 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.engCreative Commons Attribution License http://creativecommons.org/licenses/by/4.0/http://creativecommons.org/licenses/by/3.0/es/Intrusion detectionTraffic flowExploratory projection pursuitVisualizationArtificial neural networksUnsupervised learningjournal articleopen accesshttps://doi.org/10.1093/jigpal/jzac013