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

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...

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Bibliographic Details
Published in:PloS one 2024-01, Vol.19 (1), p.e0295801-e0295801
Main Authors: E L Asry, Chadia, Benchaji, Ibtissam, Douzi, Samira, E L Ouahidi, Bouabid
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Biology and Life Sciences
Classification
Comparative analysis
Computational linguistics
Computer and Information Sciences
Cybersecurity
Datasets
Deep learning
Detectors
Feature extraction
Feature selection
Health aspects
Intrusion detection systems
Language processing
Machine learning
Methodology
Methods
Natural language interfaces
Natural language processing
Neural networks
Performance enhancement
Performance evaluation
Performance measurement
Physical Sciences
Recall
Research and Analysis Methods
Support vector machines
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0295801