An Efficient Feature Reduction Comparison of Machine Learning Algorithms for Intrusion Detection System
Organization has come to recognize that applied science in network security has become very important in protecting its information. Intrusion detection present an important line of defend against all variety of attacks that can compromise the security and proper functioning of information system initiative. In this paper, the authors compared the performance of intrusion detection. The evaluation of the Intrusion Detection System (IDS) execution analysis for any given security system configuration improvement is necessary to achieve real time capability. They analyze two learning algorithms (NB and C4.5) for the task of detecting intrusions and compare their relative performances.