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Testing on the NCC-2 dataset with the Decision Tree algorithm shows that the best performance is obtained on the detection model that does not pass feature extraction with an accuracy of 99.98%, precision of 99.80%, and recall of 99.87%. Similar to the Decision Tree algorithm, the best performance obtained using the Random Forest algorithm is also obtained from models that do not go through the feature extraction process with 99.98% accuracy, 99.93% precision, and 99.80% recall. Testing with the Naïve Bayes algorithm shows that the best accuracy of 92.41% is obtained from the model with feature extraction using the Truncate SVD method, even though the highest precision and recall values are 13.35% and 96.08% come from models that do not go through the feature extraction process. The highest accuracy and precision performance in the detection model with the k-NN algorithm are obtained from the model with feature extraction using the PCA method with an accuracy value of 99.71% and a precision of 98.16%. Tests using the Logistic Regression algorithm show that only models with PCA feature extraction can detect botnets where the highest precision value is 15.30%, and the highest recall is 50.94%. |
IEEE/ICACT20230225 Slide.20
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