Intrusions are serious questions in network systems. Numerous intrusion detection techniques are present to tackle these problems but the dilemma is performance. To raise performance, it is momentous to raise the detection rates and decrease false alarm rates. The contemporary methods use Principal Component Analysis (PCA) to project features space to principal feature space and choose features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the best possible sensitivity for the classifier due to ignoring several sensitive features. Therefore, we applied a Genetic Algorithm (GA) to search the principal feature space for a subset of features with optimal sensitivity. So, in this research, a method for optimal features subset selection is proposed to overcome performance issues using PCA, GA and Multilayer Perceptron (MLP). The KDD-cup dataset is used. This method is capable to minimize amount of features and maximize the detection rates.
Key words: KDD-cup, PCA, MLP, GA, detection rate, and false alarm.
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