Fuzzing drones for anomaly detection: A systematic literature review

• Main Authors: MALVIYA, Vikas Kumar, MINN, Wei, SHAR, Lwin Khin, JIANG, Lingxiao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2025
• Subjects: drone; fuzzing; anomaly detection; MAVLink protocol; Artificial Intelligence and Robotics; Hardware Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9910; https://ink.library.smu.edu.sg/context/sis_research/article/10910/viewcontent/fuzzing_drones_for_anomaly_detection_review_computers_n_security_minor_review.pdf
• Institution: Singapore Management University

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.