High-performance intrusion detection system for networked UAVs via deep learning

Recently, Unmanned Aerial Vehicles (UAVs) have become a widely popular technology with remarkable growth and unprecedented attention. However, UAV communication networks are susceptible to various cyber-intrusions/threats due to their limited computation and communication capabilities. Such intrusions/misbehaviors tend to be processed as normal packets through the UAV communication networks. In this work, we present an autonomous intrusion detection system that can efficiently detect the malicious threats invading UAV using deep convolutional neural networks (UAV-IDS-ConvNet). Specifically, the proposed system considers encrypted Wi-Fi traffic data records of three types of commonly used UAVs: Parrot Bebop UAVs, DBPower UDI UAVs, and DJI Spark UAVs. To evaluate the developed system, we employed the UAV-IDS-2020 dataset which includes various attacks on UAV networks in unidirectional and bidirectional communication flow modes. Moreover, we emulate the context of homogeneous and heterogeneous networked UAVs. Our best experimental outcomes exhibited a victorious intrusion detection accuracy of 99.50% for the two-class classifier model (normal UAV vs. anomaly) with 2.77 ms prediction time. Besides, the proposed system was evaluated using other performance metrics including confusion matrix parameters, false alarm rate, detection precision, detection sensitivity, and prediction overhead. The performance analysis showed that our UAV-IDS-ConvNet system outperforms several recent existing intrusion detection systems developed to secure the UAV communication networks by (6–23) %.