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IEEE/ICACT20240459 Slide.18        [Big Slide]	Oral Presentation
	The highly imbalanced UNSW-NB15 dataset prompted us to implement oversampling, carefully designing RF and DNN to prevent overfitting. We evaluated performance using diverse metrics, including classification accuracy, class label accuracy using the confusion matrix, precision, recall, F1-score, and ROC curve. The consistent and convincing results obtained from both classifiers underscore the effectiveness and reliability of the proposed method.

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