Anomaly Detection in a Smart Microgrid System Using Cyber-Analytics: A Case Study

Preetha Thulasiraman (), Michael Hackett, Preston Musgrave, Ashley Edmond and Jared Seville
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Preetha Thulasiraman: Naval Postgraduate School, Monterey, CA 93943, USA
Michael Hackett: Department of Computer Science, California State University Monterey Bay, Monterey, CA 93933, USA
Preston Musgrave: Naval Postgraduate School, Monterey, CA 93943, USA
Ashley Edmond: Naval Postgraduate School, Monterey, CA 93943, USA
Jared Seville: Department of Computer Engineering and Computer Science, California State University Long Beach, Long Beach, CA 90840, USA

Energies, 2023, vol. 16, issue 20, 1-25

Abstract: Smart microgrids are being increasingly deployed within the Department of Defense. The microgrid at Marine Corps Air Station (MCAS) Miramar is one such deployment that has fostered the integration of different technologies, including 5G and Advanced Metering Infrastructure (AMI). The objective of this paper is to develop an anomaly detection framework for the smart microgrid system at MCAS Miramar to enhance its cyber-resilience. We implement predictive analytics using machine learning to deal with cyber-uncertainties and threats within the microgrid environment. An autoencoder neural network is implemented to classify and identify specific cyber-attacks against this infrastructure. Both network traffic in the form of packet captures (PCAP) and time series data (from the AMI sensors) are considered. We train the autoencoder model on three traffic data sets: (1) Modbus TCP/IP PCAP data from the hardwired network apparatus of the smart microgrid, (2) experimentally generated 5G PCAP data that mimic traffic on the smart microgrid and (3) AMI smart meter sensor data provided by the Naval Facilities (NAVFAC) Engineering Systems Command. Distributed denial-of-service (DDoS) and false data injection attacks (FDIA) are synthetically generated. We show the effectiveness of the autoencoder on detecting and classifying these types of attacks in terms of accuracy, precision, recall, and F-scores.

Keywords: cyber–physical system; cyber-anomaly; machine learning; microgrid; smart meter (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: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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