An Enhanced Network Intrusion Detection System using Dimensionality Reduction Techniques

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dc.contributor.authorOlufemi James Akinola
dc.contributor.authorSulaiman Olaniyi Abdulsalam
dc.contributor.authorMichael Favour Edafeajiroke
dc.contributor.authorKazeem Alagbe Gbolagade
dc.date.accessioned2025-01-28T21:49:06Z
dc.date.available2025-01-28T21:49:06Z
dc.date.issued2024
dc.description.abstractProtecting the privacy and confidentiality of data in computer networks is crucial, necessitating reliable intrusion detection methods. The complexity and vast volume of data present challenges to effective intrusion detection, as traditional models struggle with high-dimensional data that reduce accuracy. This study addresses these challenges by integrating Mutual Information (MI) and Linear Discriminant Analysis (LDA) as feature selection and extraction techniques respectively, to enhance the classification performance of Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CIC-IDS 2017) dataset was used to develop an Intrusion Detection System (IDS). The Results findings revealed that integrating MI+LDA with SVM yielded an accuracy of 98.84% while combining MI+LDA with KNN produced an accuracy of 90.17%. The result of the MI+LDA+SVM model based on accuracy, sensitivity, specificity, and F1-score matrixes, outperformed that of MI+LDA with KNN and other existing models. Therefore, the incorporation of MI and LDA dimensionality reduction techniques proved reliable for boosting the performance of an intrusion detection system in computer networks. Future research could apply the result of this study to other types of computer network benchmark datasets and other kinds of machine learning techniques.
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/3608
dc.language.isoen
dc.publisherKASU Journal of Computer Science
dc.titleAn Enhanced Network Intrusion Detection System using Dimensionality Reduction Techniques
dc.typeArticle
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