HYBRID NETWORK INTRUSION DETECTION SYSTEMS: A SYSTEMATIC REVIEW

Authors: Alhassan Seiba, Gaddafi Abdul-Salaam, Yaw Missah, Mohammad Hossein Anisi

Affiliation: Kwame Nkrumah University of Science and Technology, University of Essex

Category:

Information security, Network security, Threats

Keywords: Autoencoder, Intrusion Detection, Deep Learning, Feature Selection

ABSTRACT. Network Security has become a major concern to governments, businesses and individuals all over the world as cybercriminals continuously attack networks and cause harm to personal and organizational data. Different forms of Intrusion Detection Systems(IDSs) have been proposed over the years to minimize these cyberattacks. Several researchers have tried to improve upon the detection accuracy and thus, reducing false alarm rates posed by some of the IDSs. In this paper, we conducted a chronological systematic review of hybrid intrusion detection systems covering all domains. In all, about 300 recent research articles were selected in the area but only 146 articles were able to meet the given quality assurance test. A critical review of the selected articles revealed that 61% did not carry out proper feature selection as a data preprocessing step and as low as 35% handled an imbalanced dataset. We have also done extensive discussions, spanning eleven years of research works on the existing Intrusion Detection Systems.

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