Publication Type
Journal Article
Version
acceptedVersion
Publication Date
1-2025
Abstract
Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one commonly used technique for finding bugs and vulnerabilities in software programs and protocols. This article reviews various approaches where fuzzing is applied to detect bugs and vulnerabilities in UAVs. Our goal is to assess the current state-of-the-art fuzzing approaches for UAVs, which are yet to be explored in the literature. We identified open challenges that call for further research to improve the current state-of-the-art.
Keywords
drone, fuzzing, anomaly detection, MAVLink protocol
Discipline
Artificial Intelligence and Robotics | Hardware Systems
Research Areas
Information Systems and Management
Areas of Excellence
Digital transformation
Publication
Computers and Security
Volume
148
First Page
1
Last Page
45
ISSN
0167-4048
Identifier
10.1016/j.cose.2024.104157
Publisher
Elsevier
Citation
MALVIYA, Vikas Kumar; MINN, Wei; SHAR, Lwin Khin; and JIANG, Lingxiao.
Fuzzing drones for anomaly detection: A systematic literature review. (2025). Computers and Security. 148, 1-45.
Available at: https://ink.library.smu.edu.sg/sis_research/9910
Copyright Owner and License
Elsevier
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/10.1016/j.cose.2024.104157