NETWORK INTRUSION DETECTION SYSTEMS USING SUPERVISED MACHINE LEARNING CLASSIFICATION AND DIMENSIONALITY REDUCTION TECHNIQUES: A SYSTEMATIC REVIEW

Abstract

Protecting the confidentiality, integrity and availability of cyberspace and network (NW) assets has become an increasing concern. The rapid increase in the Internet size and the presence of new computing systems (like Cloud) are creating great incentives for intruders. Therefore, security engineers have to develop new technologies to match growing threats to NWs. New and advanced technologies have emerged to create more efficient intrusion detection systems using machine learning (ML) and dimensionality reduction techniques, to help security engineers bolster more effective NW Intrusion Detection Systems (NIDSs). This systematic review provides a comprehensive review of the most recent NIDS using the supervised ML classification and dimensionality reduction techniques, it shows how the used ML classifiers, dimensionality reduction techniques and evaluating metrics have improved NIDS construction. The key point of this study is to provide up-to-date knowledge for new interested researchers.