Machine Learning based Intrusion Detection System for Web-Based Attacks

Abstract

Various studies have been performed to explore the feasibility of detection of web-based attacks by machine learning techniques. False-positive and false-negative results have been reported as a major issue to be addressed to make machine learning-based detection and prevention of web-based attacks reliable and trustworthy. In our research, we tried to identify and address the root cause of the false-positive and false-negative results. In our experiment, we used the CSIC 2010 HTTP dataset, which contains the generated traffic targeted to an e-commerce web application. Our experimental results demonstrate that applying the proposed fine-tuned feature set extraction results in improved detection and classification of web-based attacks for all tested machine learning algorithms. The performance of the machine learning algorithm in the detection of attacks was evaluated by the Precision, Recall, Accuracy, and F-measure metrics. Among three tested algorithms, the J48 decision tree algorithm provided the highest True Positive rate, Precision, and Recall.