Counter Autonomy Defense for Aerial Autonomous Systems

Here, we explore methods of counter autonomy defense for aerial autonomous multi-agent systems. First, the case is made for vast capabilities made possible by these systems. Recognizing that widespread use is likely on the horizon, we assert that it will be necessary for system designers to give appropriate attention to the security and vulnerabilities of such systems. We propose a method of learning-based resilient control for the multi-agent formation tracking problem, which uses reinforcement learning and neural networks to attenuate adversarial inputs and ensure proper operation. We also devise a learning-based method of cyber-physical attack detection for UAVs, which requires no formal system dynamics model yet learns to recognize abnormal behavior. We also utilize similar techniques for time signal analysis to achieve epileptic seizure prediction. Finally, a blockchain-based method for network security in the presence of Byzantine agents is explored.