Table 35 Maximum AUC by classifier for the keylogging attack type; mean and standard deviations of AUC, (10 iterations of 5-fold cross-validation)
From: IoT information theft prediction using ensemble feature selection
Experiment name | Mean | SD |
|---|---|---|
AUC | AUC | |
LR Tuned RUS 1:3 7 Agree | 0.99639 | 0.00227 |
CatBoost RUS 1:1 6 Agree | 0.99807 | 0.00165 |
DT 4 Agree | 0.99608 | 0.00232 |
Light GBM RUS 1:1 6 Agree | 0.99737 | 0.00176 |
RF Tuned RUS 1:3 4 Agree | 0.99707 | 0.00190 |
MLP RUS 1:3 7 Agree | 0.99630 | 0.00212 |
NB Tuned RUS 1:3 7 Agree | 0.99516 | 0.00173 |
XGBoost RUS 1:1 5 Agree | 0.99693 | 0.00192 |