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2023-01-13T13:51:06Z urn:hdl:10259/7243 Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids 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 Smart grid Computational intelligence Automatic response Exploratory projection pursuit Neural network 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. 2023-01-13T13:51:06Z 2023-01-13T13:51:06Z 2020-03 info:eu-repo/semantics/article http://hdl.handle.net/10259/7243 10.3390/app10072276 2076-3417 eng Applied sciences. 2020, V. 10, n. 7, e2276 https://doi.org/10.3390/app10072276 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Atribución 4.0 Internacional MDPI