A robust intrusion detection system based on a shallow learning model and feature extraction techniques

Chadia E. L. Asry, Ibtissam Benchaji, Samira Douzi and Bouabid E. L. Ouahidi

PLOS ONE, 2024, vol. 19, issue 1, 1-31

Abstract: The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.77%. Notably, this performance is achieved by utilizing only four characteristics, indicating the efficiency of our approach. The proposed methodology achieves an accuracy of 82.86%, precision of 84.07%, recall of 77.70%, and an F1-score of 80.20% in the UNSW-NB15 dataset, using only six features. Our research findings provide substantial evidence of the enhanced performance of the proposed model compared to a traditional deep-learning model across all performance metrics.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0295801

DOI: 10.1371/journal.pone.0295801

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