Logo image
Back
Binary classification of network-generated flow data using a machine learning algorithm
Journal article   Peer reviewed

Binary classification of network-generated flow data using a machine learning algorithm

Sikha Bagui, Keenal M. Shah, Yizhi Hu and Subhash Bagui
International Journal of Information Security and Privacy, Vol.15, pp.26-43
15
2021
DOI: https://doi.org/10.4018/IJISP.2021010102
Web of Science ID: WOS:000625364200002

Metrics

66 Record Views

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.

Details

Logo image