CBDRA-IS: Centrality-Based Defense Resource Allocation for Securing Interdependent Systems

Interdependent systems, with multiple interconnected assets, face escalating cybersecurity threats from external attackers. This article explores security decision-making, operating on complex interdependent systems and proposes a security resource allocation methodology to enhance their proactive security. Using attack graphs, we model vulnerabilities and propose different defense mechanisms integrating different network analysis algorithms, including degree, betweenness, and harmonic centralities, TrustRank, and Katz centrality. We introduce Average Based Node Ranking (ABNR) to average ranks from these methods. The resource allocation methods leverage four different graph-theoretic methods. Each ranking algorithm is combined with these four allocation techniques. Our methods show low sensitivity to simultaneous attacks on interdependent systems. We validate our framework using 11 attack graphs representing real-world systems, measuring security improvements against four well-known allocation algorithms: behavioral decision-making, defense-in-depth, risk-based defense, and min-cut. Our framework outperformed the baselines in most cases, with superior outcomes confirmed by the Friedman statistical test. We show that the main components in our framework have low-time overhead. We also evaluate our framework against multi-stage attacks and cascading failures Our framework enhances security decision-making across different scenarios, including top-1 and all attack paths for different attacks. We release the implementation of our resource allocation methodology to the research community