Security breach has been recorded in high volume and has compromised several Information Systems and critical applications as well. An Intrusion Detection is the process of analyzing the events occurring in an information system in order to detect different security threats and vulnerabilities. Research and development communities are putting their extra effort for optimizing Intrusion Detection System performance as network data traffic including vulnerabilities are found to be complex and have shown dynamic properties. The idea to explore if certain classifier perform better for certain attack classes constitutes the motivation for this research work. In this research, performance of a comprehensive set of potential classifiers using Knowledge Discovery and Data (KDD99) dataset has been evaluated. Based on evaluated results, maximum accurate classifier for high attack detection rate and low false alarm rate has been chosen and suitable classifier has been proposed. The comparison of simulation result indicates that noticeable performance improvement can be achieved with the proposed classifier to detect different kinds of network attacks and security vulnerabilities.