| Title |
Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning |
| ID_Doc |
38077 |
| Authors |
Baig, Z; Syed, N; Mohammad, N |
| Title |
Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning |
| Year |
2022 |
| Published |
Future Internet, 14, 7 |
| DOI |
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. |
| Author Keywords |
drones; criminal activity; machine learning; cyber attacks |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000833163500001 |
| WoS Category |
Computer Science, Information Systems |
| Research Area |
Computer Science |
| PDF |
https://www.mdpi.com/1999-5903/14/7/205/pdf?version=1657006422
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