2026-05-31T00:10:42Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/66352022-12-19T09:57:05Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Repositorio Institucional de la Universidad de Burgos
author
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
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.
eng
Atribución 4.0 Internacional
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
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/6635/5/Quintian-LjIGPL_2022.pdf
File
MD5
20c3dced0bfedfe7b354e82cc23499c3
4908718
application/pdf
Quintian-LjIGPL_2022.pdf