Unmanned aerial vehicle (UAV) systems are vulnerable to jamming from self-interested users who utilize radio devices to disrupt UAV transmissions. The vulnerability occurs due to the open nature of air-to-ground (A2G) wireless communication networks, which may enable network-wide attacks. This paper presents two strategies to identify Jammers in UAV networks. The first strategy is based on a time series approach for anomaly detection where the available signal in the resource block is decomposed statistically to find trends, seasonality, and residues. The second is based on newly designed deep networks. The combined techniques are suitable for UAVs because the statistical model does not require heavy computation processing, but is limited to generalizing possible attacks that might occur. On the other hand, the designed deep network can classify attacks accurately, but requires more resources. The simulation considers the location and power of the jamming attacks and the UAV position related to the base station. The statistical method technique made it feasible to identify 84.38% of attacks when the attacker was at a distance of 30 m from the UAV. Furthermore, the Deep network’s accuracy was approximately 99.99 % for jamming powers greater than two and jammer distances less than 200 meters.

This research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Project Number 813391”

Cybersecurity,Convolutional Neural Networks (CNNs),Deep learning,Jamming detection,Jamming identification,UAV,Unmanned Aerial Vehicles,4G,5G