Intrusion Detection With Unsupervised Techniques for Network Management Protocols Over Smart Grids
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Intrusion Detection With Unsupervised Techniques for Network Management Protocols Over Smart GridsAuthor(s)
Date
2020-03-27Citation
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
Abstract
[Abstract] 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.
Keywords
Smart Grid
Computational intelligence
Automatic response
Exploratory projection pursuit
Neural networks
Computational intelligence
Automatic response
Exploratory projection pursuit
Neural networks
Rights
Atribución 4.0 Internacional
ISSN
2076-3417