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This table shows that models not implementing feature extraction obtained the best performance from testing on the CTU-13 dataset using the Decision Tree and Random Forest algorithms. While testing using the Naïve Bayes algorithm shows the highest accuracy obtained from the model that implements feature extraction with a value of 91.14% using four different methods. The model with the k-NN algorithm gets the highest accuracy performance of 99.29%, the highest precision of 96.87%, and the highest recall of 95.08% in the model with PCA feature selection. The detection performance of the logistic regression algorithm with the highest precision and recall values is obtained from the model that implements PCA feature extraction. |
IEEE/ICACT20230225 Slide.22
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