The present research work focuses on Intrusion Detection (ID), identifying “anomalous” patterns that may be related to an attack to a system or a network. In order to detect such anomalies, this present paper proposes the visualization of network flows for ID by applying a novel neural method called Beta Hebbian Learning (BHL). Four real-life traffic segments from the University of Twente datasets have been analysed by means of the BHL. Such datasets were gathered from a honeypot directly connected to the Internet so it is guaranteed that it contains real-attack data. Results obtained by BHL provide clear evidence of the ID System clearly separating the different types of attacks present in each dataset and outperforming other well-known projection algorithms.
CITATION STYLE
Quintián, H., Jove, E., Casteleiro-Roca, J. L., Urda, D., Arroyo, Á., Calvo-Rolle, J. L., … Corchado, E. (2021). Beta-hebbian learning for visualizing intrusions in flows. In Advances in Intelligent Systems and Computing (Vol. 1267 AISC, pp. 446–459). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-57805-3_42
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