A Survey on Security and Privacy Issues of UAVs
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Abstract (arXiv)
In the 21st century, the industry of drones, also known as Unmanned Aerial Vehicles (UAVs), has witnessed a rapid increase with its large number of airspace users. The tremendous benefits of this technology in civilian applications such as hostage rescue and parcel delivery will integrate smart cities in the future. Nowadays, the affordability of commercial drones expands its usage at a large scale. However, the development of drone technology is associated with vulnerabilities and threats due to the lack of efficient security implementations. Moreover, the complexity of UAVs in software and hardware triggers potential security and privacy issues. Thus, posing significant challenges for the industry, academia, and governments. In this paper, we extensively survey the security and privacy issues of UAVs by providing a systematic classification at four levels: Hardware-level, Software-level, Communication-level, and Sensor-level. In particular, for each level, we thoroughly investigate (1) common vulnerabilities affecting UAVs for potential attacks from malicious actors, (2) existing threats that are jeopardizing the civilian application of UAVs, (3) active and passive attacks performed by the adversaries to compromise the security and privacy of UAVs, (4) possible countermeasures and mitigation techniques to protect UAVs from such malicious activities. In addition, we summarize the takeaways that highlight lessons learned about UAVs' security and privacy issues. Finally, we conclude our survey by presenting the critical pitfalls and suggesting promising future research directions for security and privacy of UAVs.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Sep 2021.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 2.
9 papers.
- 2023Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunitiescited by 9
- 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learningcited by 9
- 2024Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approachcited by 1
- 2024Enhancing Privacy and Security of Autonomous UAV Navigationcited by 1
- 2024Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillationcited by 0
- 2025Automatic Intermodal Loading Unit Identification using Computer Vision: A Scoping Reviewcited by 0
- 2025PrivLLMSwarm: Privacy-Preserving LLM-Driven UAV Swarms for Secure IoT Surveillancecited by 0
- 2026Deontic Knowledge Graphs for Privacy Compliance in Multimodal Disaster Data Sharingcited by 0
- 2026Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learningcited by 0
- 2026Cyber Deception for Mission Surveillance via Hypergame-Theoretic Deep Reinforcement Learning21 Mar 2026
- 2026Deontic Knowledge Graphs for Privacy Compliance in Multimodal Disaster Data Sharing7 Jan 2026
- 2025PrivLLMSwarm: Privacy-Preserving LLM-Driven UAV Swarms for Secure IoT Surveillance7 Dec 2025
- 2025Automatic Intermodal Loading Unit Identification using Computer Vision: A Scoping Review22 Sep 2025
- 2024Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation1 Nov 2024
- 2024Enhancing Privacy and Security of Autonomous UAV Navigation26 Apr 2024
- 2024Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach28 Feb 2024
- 2023Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning8 Dec 2023
- 2023Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities12 Oct 2023