An Iterative Algorithm for Interdependent Estimation of Node and Link Weights in Corporate Networks for Cyber Risk Analysis

Abstract

The paper proposes a new iterative algorithm MRRW-PageRank (Mutually-Reinforced Risk-Weighted PageRank) for assessing cyber risks in corporate information systems based only on network topology. The algorithm solves the problem of determining link weights, which remains insufficiently solved in existing approaches to centrality analysis. Unlike traditional methods, where link weights are given or assumed to be the same, MRRW-PageRank establishes an interdependence between the importance of nodes and the probability of using paths to them, which models the nature of malicious paths. Node weights are updated according to the modified PageRank based on weighted links, and link weights are recalculated as a function of the importance of the target node and its input degree. The process is repeated iteratively until convergence. The algorithm is implemented as a codeless prompt based on a minimal logical framework, which provides the ability to execute in no-code environments and integrate with LLM agents. A simulation on a model network with 12 objects is presented, demonstrating the effectiveness of the method in prioritizing critical resources and identifying vulnerable penetration paths. The proposed approach is especially relevant at the stages of system design, topology audit, or initial security assessment, when there is no empirical data on vulnerabilities or behavior.