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http://dx.doi.org/10.22937/IJCSNS.2021.21.6.23

A Data-centric Analysis to Evaluate Suitable Machine-Learning-based Network-Attack Classification Schemes  

Huong, Truong Thu (Hanoi University of Science and Technology)
Bac, Ta Phuong (Soongsil University)
Thang, Bui Doan (Hanoi University of Science and Technology)
Long, Dao Minh (Hanoi University of Science and Technology)
Quang, Le Anh (Hanoi University of Science and Technology)
Dan, Nguyen Minh (Hanoi University of Science and Technology)
Hoang, Nguyen Viet (Hanoi University of Science and Technology)
Publication Information
International Journal of Computer Science & Network Security / v.21, no.6, 2021 , pp. 169-180 More about this Journal
Abstract
Since machine learning was invented, there have been many different machine learning-based algorithms, from shallow learning to deep learning models, that provide solutions to the classification tasks. But then it poses a problem in choosing a suitable classification algorithm that can improve the classification/detection efficiency for a certain network context. With that comes whether an algorithm provides good performance, why it works in some problems and not in others. In this paper, we present a data-centric analysis to provide a way for selecting a suitable classification algorithm. This data-centric approach is a new viewpoint in exploring relationships between classification performance and facts and figures of data sets.
Keywords
Machine learning; deep learning; shallow learning; datasets;
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