2026-04-29T09:57:03Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72432023-02-01T13:30:06Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Vega Vega, Rafael Alejandro Chamoso, Pablo González Briones, Alfonso Casteleiro-Roca, José-Luis Jove, Esteban Meizoso-López, María del Carmen Rodríguez-Gómez, Benigno Antonio Quintián, Héctor Herrero Cosío, Álvaro Matsui, Kenji Corchado, Emilio Calvo-Rolle, José Luis 2023-01-13T13:51:06Z 2023-01-13T13:51:06Z 2020-03 http://hdl.handle.net/10259/7243 10.3390/app10072276 2076-3417 The present research work focuses on overcoming cybersecurity problems in the Smart Grid. Smart Grids must have feasible data capture and communications infrastructure to be able to manage the huge amounts of data coming from sensors. To ensure the proper operation of next-generation electricity grids, the captured data must be reliable and protected against vulnerabilities and possible attacks. The contribution of this paper to the state of the art lies in the identification of cyberattacks that produce anomalous behaviour in network management protocols. A novel neural projectionist technique (Beta Hebbian Learning, BHL) has been employed to get a general visual representation of the traffic of a network, making it possible to identify any abnormal behaviours and patterns, indicative of a cyberattack. This novel approach has been validated on 3 different datasets, demonstrating the ability of BHL to detect different types of attacks, more effectively than other state-of-the-art methods. eng http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional Smart grid Computational intelligence Automatic response Exploratory projection pursuit Neural network Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids info:eu-repo/semantics/article