Unmanned aerial vehicle (UAV) systems are vul-
nerable 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 com-
munication 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