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

• Published in: Energies (Basel) Vol. 12; no. 19; p. 3731
• Main Authors: El Hariri, Mohamad, Harmon, Eric, Youssef, Tarek, Saleh, Mahmoud, Habib, Hany, Mohammed, Osama
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 29-09-2019; MDPI
• Subjects: Artificial intelligence; attack detection; Automation; Computer engineering; cyber security; Data acquisition; Data structures; Data transmission; Feasibility; Forecasting; IEC 61850; Intrusion; MATHEMATICS AND COMPUTING; message spoofing; microgrid; Network reliability; Neural networks; Packets (communication); process bus; Protocol (computers); sampled measured values; Statistical analysis; Supervisory control and data acquisition; Time series; Topology; Italy; Florida; United States > US; Germany
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12193731

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.