Dynamic Real-time Learning of Electromagnetic Interference Parameters for Drone Crashing Attack

Drones are increasingly used in mission-critical environments such as border surveillance, military logistics, and infrastructure inspection. However, their flexibility also introduces cybersecurity risks, as adversaries can exploit them for malicious purposes. A promising defense technique involves using electromagnetic interference to selectively neutralize rogue drones - provided their communication parameters can be precisely identified.In this work, we propose a reinforcement learning agent based on Proximal Policy Optimization (PPO) to autonomously identify the frequency-power pair susceptible to interference for an unknown drone-controller system. The agent operates in a large search space, guided by an environment modeled with dynamic hot zones. It is designed to minimize probing steps and energy consumption, making it suitable for time- and resource-constrained defensive scenarios. Experimental results in a simulated environment show that the agent achieves 90% accuracy in identifying effective jamming parameters, demonstrating its potential as a real-time, adaptive cybersecurity countermeasure for drone-based threats.