MAVIDS: an intelligent intrusion detection system for autonomous unmanned aerial vehicles

Unmanned Aerial Vehicles (UAVs) face a large threat landscape, being used in numerous industries in hostile environments while relying on wireless communication. As attacks against UAVs increase, an intelligent Intrusion Detection System (IDS) is needed to aid the UAV in identifying attacks. The UAV domain presents unique challenges for intelligent IDS development, primarily the variety of components, communication protocols, and dataset availability. A novelty-based approach to intrusion detection in UAVs is proposed by using one-class classifiers, exploiting the use of flight logs for training. The proposed technique is integrated into a fully developed IDS which operates onboard the UAV, allowing it to detect and mitigate attacks even when communication to the ground control station is lost. The approach shows promising results when faced with a number of common attacks, including macro averaged F1 scores of up to 90.57% and 94.3% for live GPS spoofing and jamming respectively.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Whelan, Jason P.

Advisor dc:contributor.advisor

• El-Khatib, Khalil

• Unmanned aerial vehicle
• Intrusion detection systems
• Machine learning
• Novelty detection
• Cyber-physical systems

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1544

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1544