IDTD-RL: An Intelligent Dual-Track Defense Risk-Sensitive Reinforcement Learning Framework for Anti-Spoofing in UAV Communications

This research presents IDTD-RL, a risk-sensitive reinforcement learning framework (a machine learning approach where an AI learns optimal actions by balancing reward and risk) designed to protect UAV communications from spoofing attacks (fraudulent signals pretending to be legitimate). The framework uses a dual-track transmission scheme and a phantom rate control mechanism to detect spoofing and disrupt attacker predictions, achieving high performance in simulations with strong interference resistance.