Abstract
Network Intrusions are critical issues in computer and network systems. Several intrusion detection approaches be present to resolve these severe problems but the major problem is performance. To increase performance, it is significant to increase the detection rates and reduce false alarm rates in the area of intrusion detection. The recent approaches use Principal Component Analysis (PCA) to project features space to principal feature space and select features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, we applied a Genetic Algorithm (GA) to search the principal feature space for genetic eigenvectors that offers a subset of features with optimal sensitivity and the highest discriminatory power. Therefore, in this research, a mechanism for optimal features subset selection is proposed to overcome performance issues using PCA, GA and Multilayer Perceptron (MLP). The KDD-cup dataset is used that is a benchmark for evaluating the security detection mechanisms. The MLP is used for classification purpose. The performance of this approach is addresses. Consequently, this method provides optimal intrusion detection mechanism which is capable to minimize amount of features and maximize the detection rates: