2026-05-31T00:10:23Zhttps://riubu.ubu.es/oai/request

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

00925njm 22002777a 4500 dc Quintián, Héctor author Jove, Esteban author Casteleiro-Roca, José-Luis author Urda Muñoz, Daniel author Arroyo Puente, Ángel author Calvo-Rolle, José Luis author Herrero Cosío, Álvaro author Corchado, Emilio author 2022-12 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. 1367-0751 http://hdl.handle.net/10259/6635 10.1093/jigpal/jzac013 1368-9894 Intrusion detection Traffic flow Exploratory projection pursuit Visualization Artificial neural networks Unsupervised learning Advanced Visualization of Intrusions in Flows by Means of Beta-Hebbian Learning