Real-Time Collaborative Intrusion Detection System in UAV Networks Using Deep Learning

Unmanned aerial vehicles (UAVs) are being used extensively in various fields. UAVs provide various services to users, including monitoring, logistics, and sensing, because of their flexible deployment and dynamic reconfigurability. However, UAV networks have become more susceptible to malicious threats because of their multiconnectivity and openness. A great effort has been made to develop an effective intrusion detection system (IDS) based on machine-learning approaches for UAVs. Unfortunately, existing methods were unable to identify real time and zero-day attacks for UAV networks. This is due to that existing methods have still used obsolete data sets and past knowledge-based detection. Also, the shortcomings of standalone IDS render them unsuitable for defending UAV networks from potential security risks. Further, the lack of precise identification for compromised UAV nodes in UAV networks poses a critical security gap, risking the entire network's integrity with the compromise of a single node. Therefore, in this work, we propose an autonomous collaborative IDS (UAV-CIDS) with a feedforward convolutional neural network (FFCNN), which accurately identifies zero-day with high accuracy. The proposed solution takes into account encoded Wi-Fi traffic logs of three popular UAVs types: 1) DBPower UDI; 2) parrot Bebop; and 3) DJI spark. Evaluation results indicate that our FFCNN model has produced outstanding results based on the UAVIDS data set with 98.23% accuracy compared to existing models. After the detection of attacks, their mitigation is equally significant. In addition, we also design and implement real-time incident response handling against cyber-attacks on UAV Networks. The incident response handling will assist in minimizing the effects of a security breach, remediate vulnerabilities and systematically secure the entire UAV networks.