Binary Classification of Network-Generated Flow Data Using a Machine Learning Algorithm

Sikha Bagui, Keenal M. Shah, Yizhi Hu and Subhash Bagui
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Sikha Bagui: University of West Florida, USA
Keenal M. Shah: University of West Florida, USA
Yizhi Hu: University of West Florida, USA
Subhash Bagui: University of West Florida, USA

International Journal of Information Security and Privacy (IJISP), 2021, vol. 15, issue 1, 26-43

Abstract: This study proposes a model for building intrusion detection systems. The dataset used, CICIDS 2017, contains 14 different attacks with 85 features for each attack. This high dimensionality of the data is a major challenge when building efficient intrusion detection systems, especially in today's big data environment, since a lot of the features are redundant. The main goal in this paper was to reduce the number of features and present a detailed discussion of the important features. For feature selection, information gain was used in an iterative way, and for classification, a machine learning algorithm, the J48 decision tree algorithm, was used. The important features for the classification of each attack were identified, and the features that were important for classifying multiple attacks were also identified and discussed.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jisp00:v:15:y:2021:i:1:p:26-43

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