A Machine Learning Surrogate for Component Criticality Ranking in Interdependent Power-Communication Networks

Cyber-physical power systems are vulnerable to cascading failures caused by interdependencies between power and communication infrastructures. Because evaluating large N-k contingency sets with a high-fidelity simulator is computationally expensive, this paper develops a machine-learning surrogate using the previously published Modified Implicative Interdependency Model (MIIM) as the ground-truth cascade simulator. The surrogate predicts contingency severity from leakage-free structural features and derives an association-based component-criticality ranking for resilience screening. On the IEEE 118-bus system, Gradient Boosting achieves a held-out Spearman correlation of 0.849 for contingency-severity ranking. Using five-fold out-of-fold predictions, the resulting component ranking achieves a Spearman correlation of 0.838 with the MIIM-derived ranking and closely approaches the observed cross-sample reproducibility level. Feature-ablation results show that inter-layer dependency features drive most of the surrogate's advantage, while end-to-end screening is approximately 158x faster than direct MIIM evaluation. The results support a two-stage workflow in which the surrogate screens candidate contingencies and components, and MIIM provides selective verification rather than directly identifying optimal hardening actions.