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Title |
A Data-centric Analysis to Evaluate Suitable Machine-Learning-based Network-Attack Classification Schemes |
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Author |
Truong Thu Huong, Ta Phuong Bac, Bui Doan Thang, Dao Minh Long, Le Anh Quang, Nguyen Minh Dan, Nguyen Viet Hoang |
| Citation |
Vol. 21 No. 6 pp. 169-180 |
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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. |
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Keywords |
Machine learning, deep learning, shallow learning, datasets. |
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