Detection of Synthetic Anomalies on an Experimentally Generated 5G Data Set Using Convolutional Neural Networks
Abstract
The research microgrid currently deployed at Marine Corps Air Station, Miramar, is leveraging Verizon's Non-Standalone (NSA) 5G communications network to provide connectivity between dispersed energy assets and the energy and water operations center (EWOC). Due to its anchor to the Verizon 4G/LTE core, the NSA network does not provide technological avenues for cyber anomaly detection. In this research, we developed a traffic anomaly detection model using supervised machine learning for the energy communication infrastructure at Miramar. We developed a preliminary cyber anomaly detection platform using a convolutional neural network (CNN). We experimentally generated a benign 5G data set using the AT and T 5G cellular tower at the NPS SLAMR facility. We injected synthetic anomalies within the data set to test the CNN and its effectiveness at classifying packets as anomalous or benign. Data sets with varying amounts of anomalous data, ranging from 10 percent to 50 percent, were created. Accuracy, precision, and recall were used as performance metrics. Our experiments, conducted with Python and TensorFlow, showed that while the CNN did not perform its best on the data sets generated, it has the potential to work well with a more balanced data set that is large enough to host more anomalous traffic.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2022
- Accession Number
- AD1200494
Entities
People
- Ashley E. Edmond
Organizations
- Naval Postgraduate School