Rating systems for enhanced cyber-security investments

Networked agents often share security risks but lack the incentive to make (sufficient) security investments if the cost exceeds their own benefit even though doing that would be socially beneficial. In this paper, we develop a systematic and rigorous framework based on rating systems for analyzing and significantly improving the mutual security of a network of agents that interact frequently over a long period of time. When designing the optimal rating systems, we explicitly consider that monitoring the agents´ investment actions is imperfect and the heterogeneity of agents in terms of both generated traffic and underlying connectivity. Our analysis shows how the optimal rating system design should adapt to different monitoring and connectivity conditions. Even though this paper considers a simplified model of the networked agents´ security, our analysis provides important and useful insights for designing rating systems that can significantly improve the mutual security of real networks in a variety of practical scenarios.