Publication:
EXPLORING DETECTION OF UNMANNED AERIAL SYSTEMS ON 5G NETWORKS VIA MACHINE LEARNING

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Authors
Gore, Alexander D.
Subjects
UAV
surveillance
drone
5G
detection
machine learning
decision trees
packet analysis
Advisors
Hale, Britta
McClure, Patrick
Date of Issue
2023-06
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
The development and implementation of 5G network technologies, including beamforming and sidelinking, will significantly improve the capabilities of adversaries performing drone operations beyond line of sight. This research explores methods for addressing the challenges presented by 5G for drone detection, such as when data is encrypted. The approach generates datasets for drone network traffic by capturing packets between a ground control station and a simulated drone. Then the individual communication flows are separated, and statistical fingerprints are constructed from the extracted temporal features: mean, median, and standard deviation of inter-arrival times, and packet direction ratio. These fingerprints are used to train and test a random forest classifier, which distinguishes drone traffic flows simulated over WiFi or Ethernet from normal 5G traffic flows with 99% accuracy and an F1 score greater than 98% in less than one tenth of a second. The classifier also detects drone traffic from data sent across a different transmission system than it was trained on with an F1 score greater than 97%. While due to tool limitations the drone data was not tested over 5G, detection aspects between drone data and other normal 5G data such as the data directional rate show promise regardless of transmission method. The proposed method's high performance and exclusive use of temporal features make it a promising direction to explore for 5G drone detection.
Type
Thesis
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Department
Computer Science (CS)
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Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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