DronLomaly: Runtime Log-based Anomaly Detector for DJI Drones

Wei Minn, Yan Naing Tun, Lwin Khin Shar, Lingxiao Jiang

Abstract: 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].