Topology-Aware Hierarchical Attack Graph Optimization for Cyber-Physical Power Systems

Cyber-physical power systems face multi-stage attacks that exploit both communication-network topology and power-grid interdependencies to reach critical substations from low-security entry points. Attack graphs systematically enumerate multi-step attack paths. However, existing approaches either ignore physical network topology or separate attack-graph construction from defense placement, limiting operational usefulness. This paper presents an enhanced topology-aware greedy (TAG) framework that couples source-to-critical attack-path search with dual-mode cyber defense and explicit cyber-physical interdependency modeling. A hierarchical attack graph is constructed directly on the physical network graph, encoding compromise probabilities conditioned on both cyber vulnerability profiles and power-grid criticality. TAG employs topology-aware candidate screening, deterministic probabilistic propagation, beam search, and one-swap local refinement, followed by a dual-mode defense package combining node hardening, micro-segmentation, and monitored-neighbor shielding. Monte Carlo experiments on the 179-bus medium-voltage feeder, IEEE 39-bus New England, IEEE 118-bus, and RTS-96 benchmarks demonstrate that TAG reduces critical-reach probability by 54.3–82.0% versus no-defense baselines (mean 70.5%), and by 30.2–51.5% versus vanilla greedy placement. A cyber-physical impact-weighted risk analysis further shows that TAG’s structural defense placement yields proportional reductions in power-flow-consequence-weighted risk. Parameter sensitivity across the segmentation/containment factor η∈[0.30,0.80] and the monitored-neighbor shielding factor ρ∈[0.05,0.30] confirms robust method superiority (68–72% risk reduction across all tested values).