Performance Enhancement of Deep Neural Network Using Feature Selection and Preprocessing for Intrusion Detection

Machine learning enables intrusion detection systems to detect network attacks adaptively and intelligently. Recently deep neural network has been investigated as such a solution owing to its high accuracy but it has limitation in real-time performance. To enhance the learning time, in this paper, we propose to use feature selection and layer configuration. We use the NSL-KDD data set, which is a refined version of the KDD CUP 99 data set and analyzed the associations between features using WEKA, a data mining tool. Our experimental results confirm that proper feature selection and layer configuration can reduce learning time while maintaining high average accuracy.