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Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning

Author

Listed:
  • Zubair Baig

    (School of Information Technology, Deakin University, Victoria 3216, Australia)

  • Naeem Syed

    (School of Information Technology, Deakin University, Victoria 3216, Australia)

  • Nazeeruddin Mohammad

    (Cybersecurity Center, Prince Mohammad Bin Fahd University, Dhahran 34754, Saudi Arabia)

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.

Suggested Citation

  • Zubair Baig & Naeem Syed & Nazeeruddin Mohammad, 2022. "Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning," Future Internet, MDPI, vol. 14(7), pages 1-19, June.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:7:p:205-:d:853079
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    References listed on IDEAS

    as
    1. Zubair Baig & Majid Ali Khan & Nazeeruddin Mohammad & Ghassen Ben Brahim, 2022. "Drone Forensics and Machine Learning: Sustaining the Investigation Process," Sustainability, MDPI, vol. 14(8), pages 1-17, April.
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