Secure Planning Against Stealthy Attacks via Model-Free Reinforcement Learning
Alper Kamil Bozkurt Yu Wang Miroslav Pajic Alper Kamil Bozkurt Yu Wang Miroslav Pajic
We consider the problem of security-aware planning in an unknown stochastic environment, in the presence of attacks on control signals (i.e., actuators) of the robot. We model the attacker as an agent who has the full knowledge of the controller as well as the employed intrusion-detection system and who wants to prevent the controller from performing tasks while staying stealthy. We formulate the ...