Abstract
Cellular connectivity for a massive number of Unmanned Aerial Vehicles (UAVs) will overcrowd the radio spectrum and cause spectrum scarcity. Incorporating Cognitive Radio (CR) with UAVs (Cognitive-UAV-Radios) has been proposed to overcome such an issue. However, the broadcasting nature of CR and the dominant line-of-sight links of UAV makes the Cognitive-UAV-Radios susceptible to jamming attacks. In this paper, we propose a framework to detect smart jammer, which locates and attacks the UAV commands with low Jamming-to-Signal-Power-Ratio (JSR). Smart jammer is more challenging than the types of jammers that always require high power values. Our work focuses on learning a Dynamic Bayesian Network (DBN) to model and analyze the signals' behaviour statistically. A Markov Jump Particle Filter (MJPF) is employed to perform predictions and consequently detect jamming signals. The results are satisfactory in terms of detection probability and false alarm rate that outperform the conventional Energy Detector approach.