Automatic Jamming Signal Classification in Cognitive UAV Radios

The integration of Cognitive Radio (CR) with Unmanned Aerial Vehicles (UAVs) is an effective step towards relieving the spectrum scarcity and empowering the UAV with a high degree of intelligence. The dynamic nature of CR and the dominant line-of-sight links of UAVs poses serious security challenges and make the CR-UAV prone to a variety of attacks as malicious jamming. Joint jammer detection and automatic jammer classification is a powerful approach against the physical layer threats by identifying multiple jammers attacking the network that realize a crucial stage towards efficient interference management. This paper proposes a novel method for joint detection and automatic classification of multiple jammers attacking with different modulation schemes. The method is based on learning a representation of the radio environment encoded in a Generalized Dynamic Bayesian Network (GDBN) whilst multiple GDBN models represent various jamming signals under different modulation schemes. The CR-UAV performs multiple predictions online in parallel and evaluates multiple abnormality measurements based on a Modified Markov Jump Particle Filter (M-MJPF) to select the best-fit model that explains the detected jammer and recognize the modulation scheme accordingly. The simulated results demonstrate that the proposed GDBN-based method outperforms Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE) in terms of classification accuracy and achieves a higher degree of explainability of its own decisions by interpreting causes and effects at hierarchical levels under the Bayesian learning and reasoning processes