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Table 27 Mean performance of 7 classifiers in terms of AUC and F1-score on datasets with features from feature group 3A

From: Detecting cybersecurity attacks across different network features and learners

Classifier Feature group 3A
AUC SD AUC F1 SD F1
CatBoost 0.88616 0.00304 0.85593 0.00363
LightGBM 0.95832 0.00088 0.93869 0.00152
Decision Tree 0.88518 0.01677 0.83763 0.01621
Logistic Regression 0.55352 0.00021 0.19546 0.00066
Naive Bayes 0.56753 0.00287 0.24000 0.00887
Random Forest 0.93496 0.01585 0.90416 0.01719
XGBoost 0.94780 0.00020 0.91189 0.00031
  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