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Table 26 Mean performance of 7 classifiers in terms of AUC and F1-score on datasets with features from feature group 2A

From: Detecting cybersecurity attacks across different network features and learners

Classifier Feature group 2A
AUC SD AUC F1 SD F1
CatBoost 0.89547 0.01241 0.86477 0.01188
LightGBM 0.95883 0.00157 0.94159 0.00127
Decision Tree 0.89876 0.00026 0.86111 0.00032
Logistic Regression 0.55609 0.00018 0.20412 0.00056
Naive Bayes 0.56993 0.00018 0.24736 0.00055
Random Forest 0.93240 0.02058 0.90543 0.02154
XGBoost 0.94174 0.00116 0.91419 0.00057
  1. Best metrics are highlighted in italics; SD AUC is the standard deviation of AUC and SD F1 is the standard deviation of the F1-score