Efficient One-to-Many Authentication With Intelligent Illegal Request Identification for UAV Networks

In Unmanned Aerial Vehicle (UAV) networks, UAVs usually perform tasks in the form of groups. When tasks change, the Ground Control Station (GCS) will assign the complemental UAV to join the group for notification or reinforcement. Since UAVs communicate over open wireless channels, secure authentication is required for complemental UAV joining the group. However, one-by-one authentication between complemental UAV and the group members leads to high overhead and delays. At the same time, when the UAV group is far away from the coverage of the GCS, the GCS is unable to assist the authentication process in real-time. To solve the above problems, we propose a one-to-many UAV authentication scheme using Identity-Based Broadcast Encryption (IBBE) and batch authentication. This scheme does not require a trusted third party to be online in real time. We also design an algorithm based on reinforcement learning for identifying illegal requests during batch authentication, enhancing efficiency and ensuring successful authentication. Our scheme meets UAV networks’ security requirements, defending against various attacks. Experimental results show that it reduces computational overhead by 55.27% and communication overhead by 23.16% compared to similar schemes. Additionally, the illegal request identification algorithm reduces identification numbers by 15.44% to 25.72% and lowers latency by 14.64% to 25.12% compared to existing methods.