An AutoML-based approach for Network Intrusion Detection
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929603751182336 |
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| author | Gyimah, Nana Kankam Mwakalonge, Judith Comert, Gurcan Siuhi, Saidi Akinie, Robert Sulle, Methusela Ruganuza, Denis Izison, Benibo Mukwaya, Arthur |
| author_facet | Gyimah, Nana Kankam Mwakalonge, Judith Comert, Gurcan Siuhi, Saidi Akinie, Robert Sulle, Methusela Ruganuza, Denis Izison, Benibo Mukwaya, Arthur |
| contents | In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML framework. Our methodology combines multiple machine learning algorithms, including LightGBM, CatBoost, and XGBoost, to enhance detection accuracy and robustness. By automating model selection, feature engineering, and hyperparameter tuning, our approach reduces the manual overhead typically associated with traditional machine learning methods. Extensive experimentation on the NSL-KDD dataset demonstrates that the stacked ensemble model outperforms individual models, achieving high accuracy and minimizing false positives. Our findings underscore the benefits of using AutoML for network intrusion detection, as the AutoML-driven stacked ensemble achieved the highest performance with 90\% accuracy and an 89\% F1 score, outperforming individual models like Random Forest (78\% accuracy, 78\% F1 score), XGBoost and CatBoost (both 80\% accuracy, 80\% F1 score), and LightGBM (78\% accuracy, 78\% F1 score), providing a more adaptable and efficient solution for network security applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15920 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An AutoML-based approach for Network Intrusion Detection Gyimah, Nana Kankam Mwakalonge, Judith Comert, Gurcan Siuhi, Saidi Akinie, Robert Sulle, Methusela Ruganuza, Denis Izison, Benibo Mukwaya, Arthur Machine Learning In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML framework. Our methodology combines multiple machine learning algorithms, including LightGBM, CatBoost, and XGBoost, to enhance detection accuracy and robustness. By automating model selection, feature engineering, and hyperparameter tuning, our approach reduces the manual overhead typically associated with traditional machine learning methods. Extensive experimentation on the NSL-KDD dataset demonstrates that the stacked ensemble model outperforms individual models, achieving high accuracy and minimizing false positives. Our findings underscore the benefits of using AutoML for network intrusion detection, as the AutoML-driven stacked ensemble achieved the highest performance with 90\% accuracy and an 89\% F1 score, outperforming individual models like Random Forest (78\% accuracy, 78\% F1 score), XGBoost and CatBoost (both 80\% accuracy, 80\% F1 score), and LightGBM (78\% accuracy, 78\% F1 score), providing a more adaptable and efficient solution for network security applications. |
| title | An AutoML-based approach for Network Intrusion Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.15920 |