2024-03-29T10:49:44Zhttps://riubu.ubu.es/oai/requestoai: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
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.
application/pdf
http://hdl.handle.net/10259/7243
eng
MDPI
Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids
info:eu-repo/semantics/article
TEXT
RIUBU. Repositorio Institucional de la Universidad de Burgos
Hispana