Analysis of Basic Compounds in a Network Intrusion Detection System using NSL-KDD Data

The increasing of security attacks and unauthorized intrusion have made network security one of the main subjects that should be considered in present data communication environment. Intrusion detection system is one of the suitable solutions to prevent and detect such attacks. This paper aims to design and implement a Network Intrusion Detection System (NIDS) based on genetic algorithm. In order to get rid of redundancy and inappropriate features principle component analysis (PCA) is useful for selecting features. The complete NSL-KDD dataset is used for training and testing data.
Number of different experiments have been done. The experimental results show that the proposed system based on GA and using PCA (for selecting five features) on NSL-KDD able to speed up the process of intrusion detection and to minimize the CPU time cost and reducing time for training and testing. C# programming language is used for system implementation.