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

TLDR

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 and accuracy comparison of the results obtained with the state of the art.

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.