@article {10.3844/jcssp.2026.2156.2188,
article_type = {journal},
title = {Ensemble Learning for Proactive Detection of Network-Intrusion-Based Insurance Fraud},
author = {Weyori, Benjamin Asubam and Tagbo, Selorm Kofi and Yakubu, Abubakar Sadik and Bonsu, Kenneth Kojo and Noah, Moesha and Appah, Eric Wiafe},
volume = {22},
number = {7},
year = {2026},
month = {Jul},
pages = {2156-2188},
doi = {10.3844/jcssp.2026.2156.2188},
url = {https://thescipub.com/abstract/jcssp.2026.2156.2188},
abstract = {We propose an ensemble learning pipeline that proactively integrates Stacking Feature Embedding (SFE) with Principal Component Analysis (PCA) and tree-based ensembles to proactively detect insurance fraud originating from network intrusions. The main contributions are: (1) the novel integration of SFE-PCA as a meta-feature construction step for tabular network flow data; (2) a sensitivity analysis that justifies PCA reduction ratios used for each dataset; and (3) a computational and ethical assessment for real-world deployment. Random Forest (RF), Extra Trees (ET), and XGBoost classifiers were trained and evaluated on benchmark intrusion datasets, specifically NSL-KDD, LYCOS-IDS2017, and CIC-IDS2018. Findings from experiments conducted on these datasets show that the proposed pipeline achieves high detection performance (AUC > 0.995) and 99.9% accuracy, while reducing feature dimensionality and resource use compared to deep baselines (CNN/LSTM). These results suggest the approach is an efficient, interpretable option for proactive intrusion-driven insurance fraud detection.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}