This paper evaluates 21 Natural Visibility Graph (NVG) topological metrics for cyber-attack classification, aiming to balance accuracy with computational efficiency. The authors apply a consensus ranking strategy integrating SHAP, Permutation Importance, Boruta, and RFE to identify the most critical descriptors. By testing subsets ranging from the top 3 to all 21 metrics, the study determines if a compact feature set can maintain classification performance while reducing overhead.
- Integrates four importance methods (SHAP, Boruta, RFE, Permutation) via consensus ranking for robust feature selection.
- Tests compact NVG metric subsets (Top3 to Top15) against the full 21-feature baseline to assess efficiency gains.
- Demonstrates that fewer topological descriptors can preserve classification capability, lowering computational cost.
- Provides a practical framework for optimizing NVG-based intrusion detection systems in resource-constrained environments.
