A fast instance reduction algorithm for intrusion detection scenarios

Journal article (2022)

Authors

Vitali Herrera-Semenets Advanced Technologies Application Center
Raudel Hernández-León Advanced Technologies Application Center
Jan van den Berg Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Copyright: © 2022 Vitali Herrera-Semenets, Raudel Hernández-León, Jan van den Berg
DOI
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https://doi.org/10.1016/j.compeleceng.2022.107963
Data mining Supervised classification Data reduction Data preprocessing Instance selection Intrusion detection
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

Abstract

We live in a world that is being driven by data. This leads to challenges of extracting and analyzing knowledge from large volumes of data. An example of such a challenge is intrusion detection. Intrusion detection data sets are characterized by huge volumes, which affects the learning of the classifier. So there is a need to reduce the size of the training sets. Fortunately, inspection and analysis of available intrusion detection data sets showed that many instances are very similar and do not provide relevant information to the classification process. This prompted to look for possibilities to use a fast algorithm that, as much as possible, removes similar instances in intrusion detection data sets while enforcing the detection rate. In this work, a new fast instance reduction algorithm is presented. The proposed algorithm provides greater efficiency during the training stage, without significantly affecting the efficacy during the intrusion detection task.