Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data.

Majdani, Farzan; Batik, Lynne; Petrovski, Andrei; Petrovski, Sergei

Abstract

This paper looks at potential vulnerabilities of the Smart Grid energy infrastructure to data injection cyber-attacks and the means of addressing these vulnerabilities through intelligent data analysis. Efforts are being made by multiple groups to provide to defence-in-depth to Smart Grid systems by developing attack detection algorithms utilising artificial neural networks that evaluate data communication between system components. The first priority of such algorithms is the detection of anomalous commands or data states; however, anomalous data states may also result from physical situations legitimately encountered by equipment. This work aims at not only detecting and alerting on anomalies, but at intelligent learning of the system behaviour to distinguish between malicious interference and anomalous system states occurring due to maintenance activity or natural phenomena, such as for instance a nearby lightning strike causing a short-circuit fault.

Citation

MAJDANI, F., BATIK, L., PETROVSKI, A. and PETROVSKI, S. 2020. Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 4, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433613

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 13th Security of information and networks international conference 2020 (SIN 2020)
• Start Date: Nov 4, 2020
• End Date: Nov 7, 2020
• Acceptance Date: Oct 27, 2020
• Online Publication Date: Nov 4, 2020
• Publication Date: Nov 30, 2020
• Deposit Date: Nov 11, 2020
• Publicly Available Date: Nov 11, 2020
• Publisher: Association for Computing Machinery (ACM)
• Peer Reviewed: Peer Reviewed
• Pages: 1-8
• DOI: https://doi.org/10.1145/3433174.3433613
• Keywords: Intelligent analysis; Contextual data; Artificial neural networks; Malicious interference; Machine learning; Smart grid; SCADA cybersecurity
• Public URL: https://rgu-repository.worktribe.com/output/996648