Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm

Giovanni Battista Gaggero; Roberto Caviglia; Alessandro Armellin; Mansueto Rossi; Paola Girdinio; Mario Marchese

doi:10.3390/s22103933

Detecting Cyberattacks on Electrical Storage Systems through Neural Network Based Anomaly Detection Algorithm

Sensors (Basel). 2022 May 23;22(10):3933. doi: 10.3390/s22103933.

Authors

Giovanni Battista Gaggero¹, Roberto Caviglia¹, Alessandro Armellin¹, Mansueto Rossi¹, Paola Girdinio¹, Mario Marchese¹

Affiliation

¹ Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture-DITEN, University of Genoa, Via Opera Pia 11A, 16145 Genoa, Italy.

Abstract

Distributed Energy Resources (DERs) are growing in importance Power Systems. Battery Electrical Storage Systems (BESS) represent fundamental tools in order to balance the unpredictable power production of some Renewable Energy Sources (RES). Nevertheless, BESS are usually remotely controlled by SCADA systems, so they are prone to cyberattacks. This paper analyzes the vulnerabilities of BESS and proposes an anomaly detection algorithm that, by observing the physical behavior of the system, aims to promptly detect dangerous working conditions by exploiting the capabilities of a particular neural network architecture called the autoencoder. The results show the performance of the proposed approach with respect to the traditional One Class Support Vector Machine algorithm.

Keywords: anomaly detection; autoencoder; cybersecurity; distributed energy resources; electrical battery storage systems; neural network.

MeSH terms

Algorithms*
Electric Power Supplies
Electricity
Neural Networks, Computer*
Support Vector Machine

Grants and funding

This research received no external funding.