The microgrid currently deployed at Marine Corps Air Station (MCAS) Miramar, California began operations in 2021. It is unique in its efforts to leverage a Verizon, fifth generation technology (5G), non-standalone communications network to provide connectivity between distributed energy resources and the MCAS energy management system (EMS). With this new technology comes additional risks in the form of cyber attacks. Therefore, novel approaches to combat this threat are necessary to protect vital energy assets. In this thesis, we discuss the development of anomalous traffic detection models that use an unsupervised machine learning autoencoder trained on benign data sets captured from an AT&T 5G cellular tower at the Naval Postgraduate School Sea Land Air Military Research facility and Raytheon hardwired Modbus network at the EMS. We created synthetic anomalies for each data set to test our autoencoder and assess its effectiveness at classifying these packets as anomalous or benign. F-score, accuracy, precision, and recall were used as performance metrics. Through experiments conducted with Python and TensorFlow, we demonstrate the autoencoder models can successfully be trained and tested using benign network data using carefully crafted synthetic anomalies. This research establishes a baseline of research for an autoencoder to be used as an effective intrusion detection system to demonstrate the utility and operability of unsupervised machine learning for use in a microgrid.Distribution Statement A. Approved for public release: Distribution is unlimited.Lieutenant Commander, United States NavyONR, Arlington, VA 2221
