DronLomaly: Runtime log-based anomaly detector for DJI drones
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 suc...
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Main Authors: | , , , |
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Format: | text |
Language: | English |
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Institutional Knowledge at Singapore Management University
2024
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Subjects: | |
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 |
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Institution: | Singapore Management University |
Language: | English |
Summary: | 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]. |
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