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This paper applies advanced mathematical aggregation techniques (Choquet integral) to optimize machine learning models for network security anomaly detection. While the application is within cybersecurity, the core contribution lies in enhancing feature selection and model efficiency using specific AI/ML methodologies rather than discussing general networking standards or consumer hardware. The work directly addresses challenges in high-dimensional data processing relevant to intelligent intrusion detection systems.
Improving Network Anomaly Detection via Choquet-Integral-Based Feature Aggregation
arXiv:2607.15389v1 Announce Type: cross Abstract: This work investigates a generalized Choquet-integral-based feature aggregation framework to improve anomaly detection in high-dimensional network traffic data. The approach combines adaptive weighting with incremental feature selection to address feature redundancy. Using Random Forest and XGBoost classifiers, we evaluate models trained with both raw and Choquet-aggregated features under varying feature subset sizes. The proposed aggregation achieves up to $7\%$ higher accuracy while reducing data volume by $77.5\%$ (from $214$~MB to $48$~MB), without degrading precision and recall. Results averaged over multiple stratified repetitions indicate that Choquet-based aggregation yields statistically significant gains ($p < 0.05$) in scenarios with limited feature availability, highlighting its suitability for real-time intrusion detection under bandwidth and feature-availability constraints.
Abstract:This work investigates a generalized Choquet-integral-based feature aggregation framework to improve anomaly detection in high-dimensional network traffic data. The approach combines adaptive weighting with incremental feature selection to address feature redundancy. Using Random Forest and XGBoost classifiers, we evaluate models trained with both raw and Choquet-aggregated features under varying feature subset sizes. The proposed aggregation achieves up to $7\%$ higher accuracy while reducing data volume by $77.5\%$ (from $214$~MB to $48$~MB), without degrading precision and recall. Results averaged over multiple stratified repetitions indicate that Choquet-based aggregation yields statistically significant gains ($p < 0.05$) in scenarios with limited feature availability, highlighting its suitability for real-time intrusion detection under bandwidth and feature-availability constraints.
Cryptography and Security (cs.CR); Machine Learning (cs.LG)
arXiv:2607.15389 [cs.CR]
(or arXiv:2607.15389v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2607.15389 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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