Abstract
AbstractIn the last few decades, the increase in the use of Web services has led to an increase in the threats of Web attacks. The severity of such Web attacks is increasing day by day. Intrusion detection systems play a crucial role in identifying Web attacks in proactive manner. There are large numbers of features present in the network traffic. Identification of relevant and irrelevant features is crucial task in machine learning. This paper proposes a Web attack detection system that consists of preprocessing, feature selection, reduced dataset, and tree-based classifiers. The system uses information gain filter method to select relevant features for the classification of Web attack. The system is tested on CIC-IDS-2017 dataset. The experimentation results show that random forest produces high precision of 74.5% for brute force, and J48 produces high precision of 63.8% and 87.5% for cross-side scripting (XSS) and SQL injection (SQLi), respectively, with 65 selected features.KeywordsWeb attackInformation gainTree-based classifiersFeature selection
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