Intrusion Detection with Unsupervised Techniques for Network Management Protocols over 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 gri...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/7/2276 |
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| author | Rafael Alejandro Vega Vega Pablo Chamoso-Santos Alfonso González Briones José-Luis Casteleiro-Roca Esteban Jove María del Carmen Meizoso-López Benigno Antonio Rodríguez-Gómez Héctor Quintián Álvaro Herrero Kenji Matsui Emilio Corchado José Luis Calvo-Rolle |
| author_facet | Rafael Alejandro Vega Vega Pablo Chamoso-Santos Alfonso González Briones José-Luis Casteleiro-Roca Esteban Jove María del Carmen Meizoso-López Benigno Antonio Rodríguez-Gómez Héctor Quintián Álvaro Herrero Kenji Matsui Emilio Corchado José Luis Calvo-Rolle |
| author_sort | Rafael Alejandro Vega Vega |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:12:42Z |
| format | Article |
| id | doaj.art-5026af2e043048d088c79fc95c4f2c42 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T10:12:42Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-5026af2e043048d088c79fc95c4f2c422023-11-16T14:26:00ZengMDPI AGApplied Sciences2076-34172020-03-01107227610.3390/app10072276Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart GridsRafael Alejandro Vega Vega0Pablo Chamoso-Santos1Alfonso González Briones2José-Luis Casteleiro-Roca3Esteban Jove4María del Carmen Meizoso-López5Benigno Antonio Rodríguez-Gómez6Héctor Quintián7Álvaro Herrero8Kenji Matsui9Emilio Corchado10José Luis Calvo-Rolle11Department of Industrial Engineering, University of A Coruña, 15403 Ferrol, SpainBISITE Research Group, University of Salamanca, Edificio I+D+i, Calle Espejo 2, 37007 Salamanca, SpainBISITE Research Group, University of Salamanca, Edificio I+D+i, Calle Espejo 2, 37007 Salamanca, SpainDepartment of Industrial Engineering, University of A Coruña, 15403 Ferrol, SpainDepartment of Industrial Engineering, University of A Coruña, 15403 Ferrol, SpainDepartment of Industrial Engineering, University of A Coruña, 15403 Ferrol, SpainDepartment of Industrial Engineering, University of A Coruña, 15403 Ferrol, SpainDepartment of Industrial Engineering, University of A Coruña, 15403 Ferrol, SpainGrupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006 Burgos, SpainFaculty of Robotics & Design, Osaka Institute of Technology, Osaka 535-8585, JapanBISITE Research Group, University of Salamanca, Edificio I+D+i, Calle Espejo 2, 37007 Salamanca, SpainDepartment of Industrial Engineering, University of A Coruña, 15403 Ferrol, SpainThe 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.https://www.mdpi.com/2076-3417/10/7/2276smart gridcomputational intelligenceautomatic responseexploratory projection pursuitneural networks |
| spellingShingle | Rafael Alejandro Vega Vega Pablo Chamoso-Santos Alfonso González Briones José-Luis Casteleiro-Roca Esteban Jove María del Carmen Meizoso-López Benigno Antonio Rodríguez-Gómez Héctor Quintián Álvaro Herrero Kenji Matsui Emilio Corchado José Luis Calvo-Rolle Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids Applied Sciences smart grid computational intelligence automatic response exploratory projection pursuit neural networks |
| title | Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids |
| title_full | Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids |
| title_fullStr | Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids |
| title_full_unstemmed | Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids |
| title_short | Intrusion Detection with Unsupervised Techniques for Network Management Protocols over Smart Grids |
| title_sort | intrusion detection with unsupervised techniques for network management protocols over smart grids |
| topic | smart grid computational intelligence automatic response exploratory projection pursuit neural networks |
| url | https://www.mdpi.com/2076-3417/10/7/2276 |
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