Learning-Based Defense against Malicious Unmanned Aerial Vehicles

Adversary unmanned aerial vehicles (UAVs) seriously threaten public security and user privacy. In this paper, we propose a reinforcement learning (RL) based defense framework to address malicious UAVs close to a target estate such as a company or an institute. This framework uses Q-learning to choose the defense policy such as jamming the global positioning system signals (GPS) and hacking, and laser shooting. According to the defense history and the current security status of the target estate, this scheme can improve the UAV defense performance in the dynamic game without being aware of the UAV attack policy and environment model in the area of interests. Simulation results show that this scheme can reduce the risk rate of the estate and improve the utility compared with the benchmark scheme against malicious UAVs.