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

00925njm 22002777a 4500 dc Vega Vega, Rafael Alejandro author Chamoso, Pablo author González Briones, Alfonso author Casteleiro-Roca, José-Luis author Jove, Esteban author Meizoso-López, María del Carmen author Rodríguez-Gómez, Benigno Antonio author Quintián, Héctor author Herrero Cosío, Álvaro author Matsui, Kenji author Corchado, Emilio author Calvo-Rolle, José Luis author 2020-03 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. http://hdl.handle.net/10259/7243 10.3390/app10072276 2076-3417 Smart grid Computational intelligence Automatic response Exploratory projection pursuit Neural network Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids