Asymptotic Security by Model-based Incident Handlers for Markov Decision Processes

TL;DR

This paper demonstrates that in control systems modeled as dynamic signaling games, the defender's belief about an attacker converges over time, ensuring asymptotic security if the system dynamics are known.

Contribution

It introduces a theoretical framework showing how model-based incident handlers can guarantee asymptotic security in Markov decision processes through belief convergence.

Findings

Defender's belief converges over time regardless of attacker's strategy.

Rational attacker behavior becomes harmless with effective defender counteractions.

Model-based defense mechanisms provide strong long-term security guarantees.

Abstract

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.