Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2022
Abstract
Drones are increasingly popular and getting used in a variety of missions such as area surveillance, pipeline inspection, cinematography, etc. While the drone is conducting a mission, anomalies such as sensor fault, actuator fault, configuration errors, bugs in controller program, remote cyber- attack, etc., may affect the drone’s physical stability and cause serious safety violations such as crashing into the public. During a flight mission, drones typically log flight status and state units such as GPS coordinates, actuator outputs, accelerator readings, gyroscopic readings, etc. These log data may reflect the above-mentioned anomalies. In this paper, we propose a novel, deep learning-based log analysis approach for detecting anomalies in the drone log that could lead to physical instabilities. We train a LSTM-based deep learning model on the normal flight logs produced by a baseline drone. Essentially, the model learns the sequential patterns of flight state units and correlations among them. The model can then be used to detect anomalies in the state units as the log entries are being recorded by the drone’s control program at runtime. In our experiments, we built detection models based on several logs produced by 3 different drone control programs, namely DJI, ArduPilot and PX4, and used them to detect anomalies in the logs. On average, our approach achieves 0.968 recall and 0.963 precision, and it can detect anomalies during runtime within a few milliseconds.
Keywords
Drone security, Anomaly detection, Log analysis, Deep learning
Discipline
Numerical Analysis and Scientific Computing | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2022 29th Asia-Pacific Software Engineering Conference (APSEC): Virtual, December 6-9: Proceedings
First Page
119
Last Page
128
ISBN
9781665455374
Identifier
10.1109/APSEC57359.2022.00024
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
SHAR, Lwin Khin; MINN, Wei; TA, Nguyen Binh Duong; FAN, Jianli; JIANG, Lingxiao; and LIM, Daniel Wai Kiat.
DronLomaly: Runtime detection of anomalous drone behaviors via log analysis and deep learning. (2022). 2022 29th Asia-Pacific Software Engineering Conference (APSEC): Virtual, December 6-9: Proceedings. 119-128.
Available at: https://ink.library.smu.edu.sg/sis_research/7545
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/APSEC57359.2022.00024