Feature Subset Selection in Intrusion Detection

Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. PCA has been employed to transform raw features into principal features space and select the features based on their sensitivity. This research applied a GA to search the principal feature space that offers a subset of features with optimal sensitivity. Based on the selected features, the classification is performed. The SVM and MLP are used for classification. This research work uses the KDD dataset. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.