Detection rate analysis for user to root attack class using correlation feature selection

Intrusion detection system (IDS) research field has grown tremendously in the past decade. Improving the detection rate of user to root (U2R) attack classes is an open research problem. Current IDS uses all data features to detect intrusions. Some of the features may be redundant to the detection process. The purpose of this empirical study is to identify important features to improve the detection rate of U2R attack class. The investigated correlation feature selection improved the overall accuracy, detection rate of U2R attack. The empirical results have given a noticeable improvement in detection rate of U2R.