Intrusion Detection and Classification Using Decision Tree-Based Feature Selection Classifiers

Feature selection method applied on an intrusion dataset is used to classify the intrusion data as normal or intrusive. Based on the performance evaluation using various feature selection algorithms and the behavior of attributes, we can distinguish the features which plays an important role for detecting intrusions. The dataset has 41 features, out of which some features play significant role in detecting the intrusions, and others do not contribute in the detection process. We have applied different feature selection techniques to extract the predominant feature that are actually effective in detecting intrusions.