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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset

Author 1: Ahmed M. Mahfouz
Author 2: Abdullah Abuhussein
Author 3: Faisal S. Alsubaei
Author 4: Sajjan G. Shiva

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.01309110

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

  • Abstract and Keywords
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Abstract: Network intrusion detection is a key step in securing today’s constantly developing networks. Various experiments have been put forward to propose new methods for resisting harmful cyber behaviors. Though, as cyber-attacks turn out to be more complex, the present methodologies fail to adequately solve the problem. Thus, network intrusion detection is now a significant decision-making challenge that requires an effective and intelligent approach. Various machine learning algorithms such as decision trees, neural networks, K nearest neighbor, logistic regression, support vector machine, and Naive Bayes have been utilized to detect anomalies in network traffic. However, such algorithms require adequate datasets to train and evaluate anomaly-based network intrusion detection systems. This paper presents a testbed that could be a model for building real-world datasets, as well as a newly generated dataset, derived from real network traffic, for intrusion detection. To utilize this real dataset, the paper also presents an ensemble intrusion detection model using a meta-classification approach enabled by stacked generalization to address the issue of detection accuracy and false alarm rate in intrusion detection systems.

Keywords: Intrusion detection system; IDS dataset; stacking ensemble ids; stacking; security; ensemble learning

Ahmed M. Mahfouz, Abdullah Abuhussein, Faisal S. Alsubaei and Sajjan G. Shiva, “Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01309110

@article{Mahfouz2022,
title = {Toward A Holistic, Efficient, Stacking Ensemble Intrusion Detection System using a Real Cloud-based Dataset},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01309110},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01309110},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Ahmed M. Mahfouz and Abdullah Abuhussein and Faisal S. Alsubaei and Sajjan G. Shiva}
}


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