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Open AccessArticle

Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids

1
Department of Industrial Engineering, University of A Coruña, 15403 Ferrol, Spain
2
BISITE Research Group, University of Salamanca, Edificio I+D+i, Calle Espejo 2, 37007 Salamanca, Spain
3
Air Institute, IoT Digital Innovation Hub (Spain), Calle Segunda 4, 37188 Salamanca, Spain
4
Research Group on Agent-Based, Social and Interdisciplinary Applications (GRASIA), Complutense University of Madrid, 28040 Madrid, Spain
5
Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, Spain
6
Faculty of Robotics & Design, Osaka Institute of Technology, Osaka 535-8585, Japan
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Appl. Sci. 2020, 10(7), 2276; https://doi.org/10.3390/app10072276
Received: 14 January 2020 / Revised: 21 March 2020 / Accepted: 22 March 2020 / Published: 27 March 2020
(This article belongs to the Special Issue Communication System in Smart Grids)
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. View Full-Text
Keywords: smart grid; computational intelligence; automatic response; exploratory projection pursuit; neural networks smart grid; computational intelligence; automatic response; exploratory projection pursuit; neural networks
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MDPI and ACS Style

Vega Vega, R.A.; Chamoso-Santos, P.; González Briones, A.; Casteleiro-Roca, J.-L.; Jove, E.; Meizoso-López, M.d.C.; Rodríguez-Gómez, B.A.; Quintián, H.; Herrero, Á.; Matsui, K.; Corchado, E.; Calvo-Rolle, J.L. Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids. Appl. Sci. 2020, 10, 2276. https://doi.org/10.3390/app10072276

AMA Style

Vega Vega RA, Chamoso-Santos P, González Briones A, Casteleiro-Roca J-L, Jove E, Meizoso-López MdC, Rodríguez-Gómez BA, Quintián H, Herrero Á, Matsui K, Corchado E, Calvo-Rolle JL. Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids. Applied Sciences. 2020; 10(7):2276. https://doi.org/10.3390/app10072276

Chicago/Turabian Style

Vega Vega, Rafael A.; Chamoso-Santos, Pablo; González Briones, Alfonso; Casteleiro-Roca, José-Luis; Jove, Esteban; Meizoso-López, María d.C.; Rodríguez-Gómez, Benigno A.; Quintián, Héctor; Herrero, Álvaro; Matsui, Kenji; Corchado, Emilio; Calvo-Rolle, José L. 2020. "Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids" Appl. Sci. 10, no. 7: 2276. https://doi.org/10.3390/app10072276

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