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Oversampling for Network Intrusion Problem with GA and k-NN

What is it about?

In order to balance a dataset, this article uses GA and k-NN to oversample the intrusive data. A proposed method outperforms a SMOTE family in almost every dataset with almost all classifiers in terms of Accuracy, Precision, and F-measure values.

Why is it important?

Our research demonstrates that, compared to a SMOTE family, newly generated intruders are produced in more independent areas. Data in an area where two classes overlap can also be handled by the proposed method. These are therefore the good aspects of the oversampling approach.

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Wattana Jindaluang
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