MAVIDS: an intelligent intrusion detection system for autonomous unmanned aerial vehicles
Date
2021-07-01
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Abstract
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.
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Unmanned aerial vehicle, Intrusion detection systems, Machine learning, Novelty detection, Cyber-physical systems