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

Author:

Vega Vega Rafael AlejandroORCID,Chamoso-Santos PabloORCID,González Briones AlfonsoORCID,Casteleiro-Roca José-LuisORCID,Jove EstebanORCID,Meizoso-López María del CarmenORCID,Rodríguez-Gómez Benigno AntonioORCID,Quintián HéctorORCID,Herrero ÁlvaroORCID,Matsui Kenji,Corchado EmilioORCID,Calvo-Rolle José LuisORCID

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.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Advanced Visualization of Intrusions in Flows by Means of Beta-Hebbian Learning;Logic Journal of the IGPL;2022-02-16

2. RECENT ADVANCES IN THE APPLICATION OF DATA SCIENCE TO INDUSTRIAL CYBERSECURITY;DYNA;2021-05-01

3. Unsupervised Online Anomaly Detection to Identify Cyber-Attacks on Internet Connected Photovoltaic System Inverters;2021 IEEE Power and Energy Conference at Illinois (PECI);2021-04-01

4. A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine;Energies;2020-09-18

5. Beta-Hebbian Learning for Visualizing Intrusions in Flows;13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020);2020-08-28

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