Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

4-2024

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].

Keywords

anomaly detection, deep learning, Drone security, log analysis

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), Lisbon, April 14-20

First Page

6

Last Page

10

ISBN

9798400705021

Identifier

10.1145/3639478.3640042

Publisher

IEEE Computer Society

City or Country

Washington, DC

Additional URL

https://doi.org/10.1145/3639478.3640042

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