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Table 28 Mean performance of 7 classifiers in terms of AUC and F1-score on datasets with features from feature group 4A

From: Detecting cybersecurity attacks across different network features and learners

Classifier Feature group 4A
AUC SD AUC F1 SD F1
CatBoost 0.62869 0.00346 0.40433 0.00786
LightGBM 0.62282 0.00243 0.38899 0.00566
Decision Tree 0.64309 0.00031 0.42947 0.00059
Logistic Regression 0.50000 0.00000 0.00000 0.00000
Naive Bayes 0.50000 0.00000 0.00000 0.00000
Random Forest 0.65889 0.00030 0.46665 0.00061
XGBoost 0.64208 0.00030 0.42912 0.00065
  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