Security-centric node identification in complex networks

Ensuring network security in complex and dynamic environments has become a critical challenge due to the increasing proliferation of Internet of Things (IoT) devices and decentralized architectures such as Fog Computing. Traditional node identification methods primarily focus on either network centrality measures or security metrics in isolation, which limits their effectiveness in detecting security-critical nodes. In this paper, we propose a novel Security-Centric Node Identification method that integrates multiple centrality measures with security-related metrics and dynamic factors to compute a comprehensive Security Centrality (SC) for each node. Unlike conventional approaches, our method accounts for both structural importance and security vulnerabilities by incorporating degree, betweenness, closeness, and eigenvector centralities with real-time security risk assessments and dynamic network conditions. To achieve this, we develop a mathematical model to compute SC, introduce efficient algorithms for identifying critical nodes, and implement an incremental update mechanism to enhance adaptability in real-time networks. Our experimental evaluation on various network topologies, including random, scale-free, small-world, and real-world networks, demonstrates that the proposed method effectively identifies security-critical nodes with high detection accuracy while maintaining a low false-positive rate. The results show that incorporating dynamic factors significantly improves the robustness of node identification, making our method highly adaptable to real-world network security scenarios.