DronLomaly: Runtime log-based anomaly detector for DJI drones

• Main Authors: MINN, Wei, YAN, Naing Tun, SHAR, Lwin Khin, JIANG, Lingxiao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: anomaly detection; deep learning; Drone security; log analysis; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/8887; https://ink.library.smu.edu.sg/context/sis_research/article/9890/viewcontent/3639478.3640042_pvoa_cc_by.pdf
• Institution: Singapore Management University

We present an automated tool for realtime detection of anomalous behaviors while a DJI drone is executing a flight mission. The tool takes sensor data logged by drone at fixed time intervals and performs anomaly detection using a Bi-LSTM model. The model is trained on baseline flight logs from a successful mission physically or via a simulator. The tool has two modules --- the first module is responsible for sending the log data to the remote controller station, and the second module is run as a service in the remote controller station powered by a Bi-LSTM model, which receives the log data and produces visual graphs showing the realtime flight anomaly statuses with respect to various sensor readings on a dashboard. We have successfully evaluated the tool on three datasets including industrial test scenarios. DronLomaly is released as an open-source tool on GitHub [10], and the demo video can be found at [17].