Development of an Intrusion Detection System using Mayfly Feature Selection and Artificial Neural Network Algorithms

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dc.contributor.authorSulaiman Olaniyi Abdulsalam
dc.contributor.authorRasheed Abiodun Ayofe
dc.contributor.authorMichael Favour Edafeajiroke
dc.contributor.authorJumoke Falilat Ajao
dc.contributor.authorRonke Seyi Babatunde
dc.date.accessioned2025-01-29T04:54:05Z
dc.date.available2025-01-29T04:54:05Z
dc.date.issued2024-06-24
dc.description.abstractProtecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of detecting intrusion. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study proposed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifier. The model was also tested without a Mayfly Algorithm (MA). The efficiency of the model was determined through a comparison of its Accuracy, Specificity, Precision, Negative predictive value, False positive rate, False discovery rate, False negative rate, Sensitivity, and F1- score. The experimental outcomes revealed that the proposed model is more efficient than existing models when implemented on the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CIC-IDS 2017) dataset. Accuracy of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. The proposed model demonstrated superior accuracy compared to existing studies. Its robustness is due to employing mayfly techniques that combine the strengths of PSO, GA, and FA for optimal feature selection. This research presents a dependable dimensionality reduction model useful for intrusion detection and improving security in computer networks.
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/3707
dc.language.isoen
dc.publisherLAUTECH Journal of Engineering and Technology
dc.titleDevelopment of an Intrusion Detection System using Mayfly Feature Selection and Artificial Neural Network Algorithms
dc.typeArticle
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