A Robust Stackelberg Game for Cyber-Security Investment in Networked Control Systems

TL;DR

This paper develops a game-theoretic framework for cyber-security investment in networked control systems, balancing attack and defense strategies to maintain system performance under resource constraints.

Contribution

It introduces a cost-based Stackelberg equilibrium approach and robust defense algorithms for cyber-security in NCSs, validated on electric power system examples.

Findings

Reliable defense is possible unless defender's resources are too limited.

The methods are robust to model uncertainties and time-varying conditions.

Genetic algorithms effectively compute strategies in large systems.

Abstract

We present a resource-planning game for cyber-security of networked control systems (NCS). The NCS is assumed to be operating in closed-loop using a linear state-feedback $\mathcal{H}_2$ controller. A zero-sum, two-player Stackelberg game (SG) is developed between an attacker and a defender for this NCS. The attacker aims to disable communication of selected nodes and thereby render the feedback gain matrix to be sparse, leading to degradation of closed-loop performance, while the defender aims to prevent this loss by investing in the protection of targeted nodes. Both players trade their $\mathcal{H}_2$-performance objectives for the costs of their actions. The standard backward induction method is modified to determine a cost-based Stackelberg equilibrium (CBSE) that saves the players' costs without degrading the control performance. We analyze the dependency of a CBSE on the relative budgets of the players as well as on the node "importance" order. Moreover, a robust-defense method is developed for the realistic case when the defender is not informed about the attacker's resources. The proposed algorithms are validated using examples from wide-area control of electric power systems. It is demonstrated that reliable and robust defense is feasible unless the defender's resources are severely limited relative to the attacker's resources. We also show that the proposed methods are robust to time-varying model uncertainties and thus are suitable for long-term security investment in realistic NCSs. Finally, we employ computationally efficient genetic algorithms (GA) to compute the optimal strategies of the attacker and the defender in realistic large power systems.