Detection of Cyberattacks in an Software-Defined UAV Relay Network

Unmanned aerial vehicles (UAVs), e.g., drones, have become crucial assets in the military's fleet of vehicles. UAVs can provide limited bandwidth for tactical communications and can act as relays over battlefields. Modern drones provide much higher bandwidth with dynamic antenna capabilities that would be useful in communicating around obstacles, such as urban corridors formed by rows of tall buildings that limit terrestrial lines of sight and attenuate high frequencies. While it is still more likely that one UAV is used for this purpose, a well-managed cluster of UAVs could increase the functionality of the entire terrestrial-drone network. Software-defined wireless networking (SDWN) is recognized as an effective way to manage distributed wireless networks. This paper proposes to use software-defined UAV networks (SD-UAV) to provide well-coordinated, secure communication resources and relaying capabilities to on-the-ground soldiers, military vehicles, and assets in an urban, signal-challenged environment. A mobility and packet delivery analysis is performed to determine the flow of packets through the simulated network, and, to maintain secure communication, a multi-cyberattack detection model is proposed to defend against jamming, black hole, and gray hole attack using the Light Gradient Boosting (LightGBM) machine learning (ML) algorithm. Results show our model can provide an average of greater than 98% detection accuracy, precision, recall, and F1-scores.