NIPSA Intrusion Classification

Abstract

With the extensive growth of the Internet, network security has become more important. The growing proportion of network intrusions has impacted network security more than ever. The number of network attacks is significantly increasing. One major method of defence against these attacks is the intrusion detection and prevention system. For an in-depth understanding of this system, a conceptual framework for intrusion classification must be developed. This classification framework could be used to develop the algorithm for a Network-based Intrusion Detection System (NIDS) and a Network-based Intrusion Prevention System (NIPS). Up until this point, a structured framework or standard for classifying network intrusion has limitations to identify the intrusion. To comprehend the threats posed by intrusion, this paper proposes a new way for classifying network intrusion efficiently. Experimental results indicate that the proposed NIPSA intrusion classification can identify intrusion with an overall rate of accuracy of 99.42%, a 0.3% FP rate, and a 0.6% FN rate. The NIPSA intrusion classification can help organizations to implement Network-Based Intrusion Detection and Prevention System for better performance in the future. © School of Engineering, Taylor's University.