Performance Analysis Of Different Feature Selection Methods In Intrusion Detection

In today’s era detection of security threats that are commonly referred to as intrusion, has become a very important and critical issue in network, data and information security. Highly confidential data of various organizations are present over the network so in order to preserve that data from unauthorized users or attackers a strong security framework is required. Intrusion detection system plays a major role in providing security to computer networks. An Intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside the organization. The Intrusion detection system deals with large amount of data whichcontains various irrelevant and redundant features resulting in increased processing time and low detection rate. Therefore feature selection plays an important role in intrusion detection. There arevarious feature selection methods proposed in literature by different authors. In this paper a comparative analysis of different feature selection methods are presented on KDDCUP’99 benchmark dataset and their performance are evaluated in terms of detection rate, root mean square error and computational time.