2024-03-29T12:45:25Zhttps://riubu.ubu.es/oai/requestoai: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