Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2025

Abstract

Configurations are supported by most flight control systems, allowing users to control a flying drone adapted to complexities such as environmental changes or mission alterations. Such an advanced functionality also introduces a significant problem—misconfiguration settings. It may cause drone instability, threaten drone safety, and potentially lead to substantial financial loss. However, detecting and rectifying misconfigurations across different flight control systems is challenging because (1) (mis)configuration-related code snippets might be syntactically correct and thus hard to identify through traditional code analysis; (2) the response to each configuration varies under different flying scenarios.In this article, we propose and implement a novel rectification approach, Nyctea, to detect instability caused by misconfigurations and conduct an on-the-fly rectification. Nyctea first continuously inspects state changes over consecutive time intervals and calculates the overall deviations to determine whether a drone is in a transition of instability to control loss. When a potential instability is reported, Nyctea instantly invokes a pre-trained intelligent agent to automatically generate proper configurations and then re-configure the drone against entering a state of loss of control. This process of reconfiguration is conducted iteratively until the instability is eliminated. We integrated Nyctea with the widely used flight control system, Ardupilot and PX4. The simulated and practical experiment results showed that Nyctea successfully eliminates instabilities caused by 85% of misconfigurations. For each misconfiguration, Nyctea averagely generated 4 to 5 configurations to achieve a successful rectification.

Keywords

Drone security, instability rectification, reinforcement learning

Discipline

Software Engineering

Areas of Excellence

Digital transformation

Publication

ACM Transactions on Software Engineering and Methodology

Volume

34

Issue

4

First Page

1

Last Page

29

ISSN

1049-331X

Identifier

10.1145/3702994

Publisher

Association for Computing Machinery (ACM)

Additional URL

https://doi.org/10.1145/3702994

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