The IEC 61850 Sampled Measured Values Protocol: Analysis, Threat Identification, and Feasibility of Using NN Forecasters to Detect Spoofed Packets

Mohamad El Hariri, Eric Harmon, Tarek Youssef, Mahmoud Saleh, Hany Habib and Osama Mohammed
Additional contact information
Mohamad El Hariri: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Eric Harmon: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Tarek Youssef: Department of Electrical and Computer Engineering, University of West Florida, Pensacola, Fl 32514, USA
Mahmoud Saleh: Department of Electrical and Computer Engineering, Florida Polytechnic University, Lakeland, FL 33805, USA
Hany Habib: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Osama Mohammed: Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA

Energies, 2019, vol. 12, issue 19, 1-24

Abstract: The operation of the smart grid is anticipated to rely profoundly on distributed microprocessor-based control. Therefore, interoperability standards are needed to address the heterogeneous nature of the smart grid data. Since the IEC 61850 emerged as a wide-spread interoperability standard widely accepted by the industry, the Sampled Measured Values method has been used to communicate digitized voltage and current measurements. Realizing that current and voltage measurements (i.e., feedback measurements) are necessary for reliable and secure noperation of the power grid, firstly, this manuscript provides a detailed analysis of the Sampled Measured Values protocol emphasizing its advantages, then, it identifies vulnerabilities in this protocol and explains the cyber threats associated to these vulnerabilities. Secondly, current efforts to mitigate these vulnerabilities are outlined and the feasibility of using neural network forecasters to detect spoofed sampled values is investigated. It was shown that although such forecasters have high spoofed data detection accuracy, they are prone to the accumulation of forecasting error. Accordingly, this paper also proposes an algorithm to detect the accumulation of the forecasting error based on lightweight statistical indicators. The effectiveness of the proposed methods is experimentally verified in a laboratory-scale smart grid testbed.

Keywords: artificial intelligence; attack detection; cyber security; microgrid; process bus; IEC 61850; sampled measured values; neural networks; forecasting; message spoofing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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