Development of an Intrusion Detection System in Web Applications Using C-Means and Decision Tree Algorithm
| dc.contributor.author | Isiaka, Rafiu Mope | |
| dc.contributor.author | Popoola, Damilola David | |
| dc.date.accessioned | 2025-01-25T06:51:53Z | |
| dc.date.available | 2025-01-25T06:51:53Z | |
| dc.date.issued | 2023-03-01 | |
| dc.description.abstract | Intrusion 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.issn | 2221-0997 | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/3305 | |
| dc.language.iso | en | |
| dc.publisher | International Journal of Applied Science and Technology | |
| dc.title | Development of an Intrusion Detection System in Web Applications Using C-Means and Decision Tree Algorithm |