Cyber-physical threats to unmanned aerial vehicles (UAVs) involve manipulating control, communication, and sensor data through evil actions that an intruder can carry out. Drone cyber-physical systems rely on wireless connections, which can be attacked in many ways. The intruder can take advantage of the inherent vulnerabilities in the Global Positioning System (GPS) to spoof it and generate a fake signal that is transmitted to the receiver. The routes of UAV dynamic movements that are predicted, hijacked, and mitigated are presented in this work. The dynamic and adaptive GPS spoofing threat detection and mitigation system for networked UAVs has been proposed in this research. The suggested design technique finds the deviation in the flying path at different altitudes that cyber threats could cause. Drone route design based on multi-logit regression has been suggested to consider the spoofing errors between the waypoints of the expected and spoofed (hijacked) path to predict elevations and angles. By generating the appropriate thrusts of the drone’s rotor and then responding with a new rectified path to a spoof position detection, a proportional and derivative (PD) control has been developed for the attitude and position control of drones. For the swarm of drones, blockchain-based delegated proof of location (DPoL) as a consensus mechanism with GPS spoofing mitigation capability, at various waypoints and intervals, has been proposed. The effectiveness of the proposed work has been tested with simulation work supported by UAV testbed.