Development of an Intrusion Detection System in Web Applications Using C Means and Decision Tree Algorithm

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dc.contributor.authorIsiaka, Mope Rafiu
dc.contributor.authorPopoola, Damilola David
dc.date.accessioned2026-05-16T19:50:49Z
dc.date.available2026-05-16T19:50:49Z
dc.date.issued2023-03-05
dc.description.abstractIntrusion detection is extremely important for online applications and for determining whether there has been a hostile entrance into the website. The aim of this research is to provide a machine learning technique for detecting intrusion in a web application. Machine learning models such as C-means, Decision Tree and Support Vector Machine were utilized to create an intrusion detection system. The study used the CIC-IDS 2018 intrusion dataset (Friday-Working Hours-Afternoon Ddos.pcap ISCX). The data was initially sent to Decision tree and SVM which had accuracy of 99.97% and 99.77%, respectively. The raw data was next transferred into the c-means clustering approach, which had an accuracy of 99.99%. The goal of the clustering technique used is to improve the system’s accuracy, and the results were assessed using performance metrics like accuracy, sensitivity, precision, specificity, F1-score as well as accuracy comparison of the results obtained with the state of the art.
dc.identifier.issn2221-0997
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/7257
dc.language.isoen
dc.publisherInternational Journal of Applied Science and Technology
dc.titleDevelopment of an Intrusion Detection System in Web Applications Using C Means and Decision Tree Algorithm
dc.typeArticle
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