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A proposed method for detecting network intrusion using an ensemble learning (stacking -voting) approach with unbalanced data

By
Anouar Bachar ,
Anouar Bachar

Laboratory of Science and Technology for the Engineer, LaSTI-ENSA, Sultan Moulay Slimane University, Khouribga 25000, Morocco

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Omar EL Bannay ,
Omar EL Bannay

Laboratory of Science and Technology for the Engineer, LaSTI-ENSA, Sultan Moulay Slimane University, Khouribga 25000, Morocco

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Abstract

The use of computer networks has become necessary in most human activities. However, these networks are exposed to potential threats affecting the confidentiality, integrity, and availability of data. Nowadays, the security of computer networks is based on tools and software such as antivirus software. Among the techniques used for machine protection, firewalls, data encryption, etc., were mentioned. These techniques constitute the first phase of computer network security. However, they remain limited and do not allow for full network protection. In this paper, a Network Intrusion Detection System (NIDS) was proposed for binary classification. This model was based on ensemble learning techniques, where the base models were carefully selected in a first layer. Several machine learning algorithms were individually studied to choose the best ones based on multiple metrics, including calculation speed. The SMOTE technique was used to balance the data, and cross-validation was employed to mitigate overfitting issues. Regarding the approaches used in this research, a stacking and voting model was employed, trained, and tested on a UNSW-NB15 dataset. The stacking classifier achieved a higher accuracy of 96%, while the voting approach attained 95.6%.

 

How to Cite

1.
Bachar A, EL Bannay O. A proposed method for detecting network intrusion using an ensemble learning (stacking -voting) approach with unbalanced data. Data and Metadata [Internet]. 2024 Jun. 22 [cited 2024 Jul. 15];3:297. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/297

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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