Asymptotic Security by Model-based Incident Handlers for Markov Decision Processes
This work addresses security for cyber-physical control systems, but it appears incremental as it builds on existing game-theoretic models without introducing major new methods.
The study tackles the problem of defending control systems against cyber attacks by analyzing the asymptotic behavior of model-based incident handlers in a dynamic signaling game. It shows that the defender's belief about the attacker's existence converges over time, leading the attacker's rational behavior to become harmless when effective counteractions are in place.
This study investigates general model-based incident handler's asymptotic behaviors in time against cyber attacks to control systems. The attacker's and the defender's dynamic decision making is modeled as an equilibrium of a dynamic signaling game. It is shown that the defender's belief on existence of an attacker converges over time for any attacker's strategy provided that the stochastic dynamics of the control system is known to the defender. This fact implies that the rational behavior of the attacker converges to a harmless action as long as the defender possesses an effective counteraction. The obtained result supports the powerful protection capability achieved by model-based defense mechanisms.