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Title	Securing The Smart City Airspace: Drone Cyber Attack Detection Through Machine Learning
ID_Doc	47811
Authors	Baig Z.; Syed N.; Mohammad N.
Year	2022
Published	Future Internet, 14, 7
DOI	http://dx.doi.org/10.3390/fi14070205
Abstract	Drones are increasingly adopted to serve a smart city through their ability to render quick and adaptive services. They are also known as unmanned aerial vehicles (UAVs) and are deployed to conduct area surveillance, monitor road networks for traffic, deliver goods and observe environmental phenomena. Cyber threats posed through compromised drones contribute to sabotage in a smart city’s airspace, can prove to be catastrophic to its operations, and can also cause fatalities. In this contribution, we propose a machine learning-based approach for detecting hijacking, GPS signal jamming and denial of service (DoS) attacks that can be carried out against a drone. A detailed machine learning-based classification of drone datasets for the DJI Phantom 4 model, compromising both normal and malicious signatures, is conducted, and results obtained yield advisory to foster futuristic opportunities to safeguard a drone system against such cyber threats. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
Author Keywords	criminal activity; cyber attacks; drones; machine learning

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