https://jjcit.org/paper/147
NETWORK INTRUSION DETECTION SYSTEMS USING SUPERVISED MACHINE LEARNING CLASSIFICATION AND DIMENSIONALITY REDUCTION TECHNIQUES: A SYSTEMATIC REVIEW
10.5455/jjcit.71-1629527707
Zein Ashi,Laila Aburashed,Mahmoud Al-Qudah,Abdallah Qusef
Network intrusion detection,Machine learning,Supervised learning,Dimensionality,Systematic review
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21-Aug.-2021
4-Nov.-2021
14-Nov.-2021
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.