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

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

Authors

Farzan Majdani

Lynne Batik

Andrei Petrovski

Sergei Petrovski



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 International conference on security of information and networks (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], (accepted). To be made available from: https://doi.org/10.1145/3433174.3433613

Conference Name 13th International conference on security of information and networks (SIN 2020)
Start Date Nov 4, 2020
End Date Nov 7, 2020
Acceptance Date Nov 1, 2020
Deposit Date Nov 11, 2020
Publicly Available Date Nov 11, 2020
Publisher Association for Computing Machinery
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

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Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/





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