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Intrusion detection systems (IDSs) are essential elements of IT systems.Their key component is a classification module that continuously evaluates somefeatures of the network traffic and identifies possible threats. Its efficiencyis greatly affected by the right selection of the features to be monitored.Therefore, the identification of a minimal set of features that are necessaryto safely distinguish malicious traffic from benign traffic is indispensable inthe course of the development of an IDS. This paper presents the preprocessingand feature selection workflow as well as its results in the case of theCSE-CIC-IDS2018 on AWS dataset, focusing on five attack types. To identify therelevant features, six feature selection methods were applied, and the finalranking of the features was elaborated based on their average score. Next,several subsets of the features were formed based on different rankingthreshold values, and each subset was tried with five classification algorithmsto determine the optimal feature set for each attack type. During theevaluation, four widely used metrics were taken into consideration.

Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System