Differ Feature Reduction Techniques for Intrusion Detection System
1,2 College of education in Computer Science, AL-Mustansiriya University, Iraq. Baghdad. Abstract— Due to a growing number of computer network in recently years there has been an increasing interest in the intrusion detection system (IDS) that it monitors the activity occurring in a computer network and analyze them for recognizing intrusions to protect the computer network . Most of existing IDS use all of features available in the network packet to analyze and look instructive pattern while some of these features are redundant and irrelevant this is time consuming and performance degrading process. In this paper three different dimensionality reduction algorithms Principal Component Analysis (PCA),Linear Discriminate Analysis( LDA) and Singular Value Decomposition (SVD), were used and an experimental result were tested to get the efficient algorithm that when applied with classification algorithm gives the highest result. Classification algorithms were used Back Propagation Neural Network (BPNN) to detect different attack types. Keywords— IDS, KDDCUP1999, Feature Reduction, Attack Detection.