An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge from previous experiences facilitate the radio spectrum perception and allow the radio to detect abnormal behaviours caused by jamming attacks. Detecting abnormalities realize a fundamental step towards growing up incrementally the radio’s long-term memory. Deviations from predictions estimated during abnormal situations are used to characterize jammers at multiple levels and discover their dynamic behavioural rules. Besides, a proactive consequence can be drawn after estimating the jammer’s signal to act efficiently by mitigating its effects on the received stimuli. Simulation results show that the introduction of the novel SA functionalities with the proposed HDBN framework provides the high accuracy of characterizing, detecting and predicting the jammer’s activities.