Yizhi Huang, Georgia Institute of Technology; David Oygenblik, Georgia Tech; Runze Zhang, Mingxuan Yao, Muhammad Ibrahim, Burak Sahin, Haichuan Xu, Saman Zonouz, and Brendan Saltaformaggio, Georgia Institute of Technology
In dynamic environments, unmanned aerial vehicles (UAVs) often utilize online learning to refine their machine learning (ML) model's decision boundaries for improved performance. Unfortunately, when the UAV becomes irrecoverable or unavailable (e.g., a crash), a forensic investigator would be left helpless to determine if the UAV's online learning caused the crash. This paper proposes a novel forensic technique, called FIRA, that can establish causal connections from ML models to UAV system components. FIRA sends back in-flight online learning updates and telemetry data (even when bandwidth is limited) and determines whether the crash can be attributed to the online learning model. We applied FIRA to 48 UAV crash scenarios using two widely adopted UAV control programs: PX4 and ArduPilot. Across four types of UAV missions, FIRA investigated 12 accidents (each) in which a backdoored online learning model was the cause of the crash, and FIRA was able to correctly attribute the model to the crash with 95.8% accuracy.
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