A Knowledgeable Feature Selection Based on Set Theory for Web Intrusion Detection System

Web intrusion detection systems are security programs to decide whether events and activities occurring in a Web application or network are legitimate. The objective of Web IDS is to identify intrusions with high false alarms and low detection rate while consuming minor properties. Similarly, intelligent Web IDS have snags of concert efficiency, false positive, and false negative, while today’s advance Web page creation approaches are also facing training/learning in the clouds, great false alarms, and truncated detection rate. In this paper, an efficient feature selection approach is proposed by selecting an optimum subset of features. Hybrid feature selection relevance algorithm is used for optimum subset feature selection that decouples relevance and redundancy analysis. Empirical results show that the new proposed system gives better and robust representation of an ideal intrusion detection system while having the reduced total number of features, truncated false alarms, great detection rate, and least computation cost.