The DOI matches its Crossref record.
Securing commercial WiFi-based UAVs from common security attacks
Abstract
We posit that commercial Wi-Fi-based unmanned aerial vehicles (UAV) are vulnerable to common and basic security attacks, capable by beginner to intermediate hackers. We do this by demonstrating that the standard ARDiscovery Connection process and the Wi-Fi access point used in the Parrot Bebop UAV are exploitable such that the UAV's ability to fly can be disrupted mid-flight by a remote attacker. We believe that these vulnerabilities are systemic in Wi-Fi-based Parrot UAVs. Our approach observed the normal operation (i.e., ARDiscovery Connection process over Wi-Fi) of the Parrot Bebop UAV. We then used a fuzzing technique to discover that the Parrot Bebop UAV is vulnerable to basic denial of service (DoS) and buffer-overflow attacks during its ARDiscovery Connection process. The exploitation of these vulnerabilities could result in catastrophic and immediate disabling of the UAV's rotors midflight. Also, we discovered that the Parrot Bebop UAV is vulnerable to a basic ARP (Address Resolution Protocol) Cache Poisoning attack, which can disconnect the primary mobile device user and in most cases cause the UAV to land or return home. Based on the literature and our own penetration testing, we assert that Wi-Fi-based commercial UAVs require a comprehensive security framework that utilizes a defense-in-depth approach. This approach would likely mitigate security risks associated with the three zero-day vulnerabilities described in this paper as well as other vulnerabilities reported in the literature. This framework will be effective for Parrot Wi-Fi-based commercial UAVs and likely others with similar platforms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Security analysis of drones systems: Attacks, limitations, and recommendations
Yaacoub, J.-P., Noura, H., Salman, O., & Chehab, A. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100218 · Cited by 727
Cognition in UAV-Aided 5G and Beyond Communications: A Survey
Ullah, Z., Al-Turjman, F., & Mostarda, L. (2020) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2020.2968311 · Cited by 226
A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
Castrillo, V. U., Manco, A., Pascarella, D., & Gigante, G. (2022) · Drones · doi:10.3390/drones6030065 · Cited by 131
Reliable and Efficient Data Collection in UAV-based IoT Networks
Joshi, P., Kalita, A., & Gurusamy, M. (2023) · arXiv · doi:10.48550/arxiv.2311.05303 · Cited by 61
“One limitation of Wi-Fi is its handling of mobility and hand-off between BSs, constraining the operational range of drones to a few miles within direct access to the AP [43].”
Metadata
- Authors
- Hooper, M., Tian, Y., Zhou, R., Cao, B., Lauf, A. P., Watkins, L., Robinson, W. H., & Alexis, W.
- Venue
- MILCOM 2016 - 2016 IEEE Military Communications Conference
- Publisher
- IEEE
- Published
- 2016-11
- Pages
- 1213-1218
- OpenAlex
- W2566869048
- Cited by
- 163
- References
- 0
- FWCI
- 12.28
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Securing commercial WiFi-based UAVs from common security attacks. https://app.cochat.ai/papers/10.1109/milcom.2016.7795496
- The paper
- Hooper, M., Tian, Y., Zhou, R., Cao, B., Lauf, A. P., Watkins, L., Robinson, W. H., & Alexis, W. (2016). Securing commercial WiFi-based UAVs from common security attacks. MILCOM 2016 - 2016 IEEE Military Communications Conference. https://doi.org/10.1109/milcom.2016.7795496
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.