RT info:eu-repo/semantics/article
T1 Advanced Visualization of Intrusions in Flows by Means of Beta-Hebbian Learning
A1 Quintián, Héctor
A1 Jove, Esteban
A1 Casteleiro-Roca, José-Luis
A1 Urda Muñoz, Daniel
A1 Arroyo Puente, Ángel
A1 Calvo-Rolle, José Luis
A1 Herrero Cosío, Álvaro
A1 Corchado, Emilio
K1 Intrusion detection
K1 Traffic flow
K1 Exploratory projection pursuit
K1 Visualization
K1 Artificial neural networks
K1 Unsupervised learning
K1 Informática
K1 Computer science
AB 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.
PB Oxford University Press
SN 1367-0751
YR 2022
FD 2022-12
LK http://hdl.handle.net/10259/6635
UL http://hdl.handle.net/10259/6635
LA eng
NO Funding for open access charge: Universidade da Coruña/CISUG.
DS Repositorio Institucional de la Universidad de Burgos
RD 24-dic-2024