Effect Improved for High-Dimensional and Unbalanced Data Anomaly Detection Model Based on KNN-SMOTE-LSTM

Table 7

Comparison of detection results of unbalanced algorithm.

Model	Precision	Recall	F-score	AUC
LSTM	0.8723	0.8255	0.8483	0.9132
ADASYN + LSTM	0.0301	0.7718	0.0580	0.9246
SMOTE + LSTM	0.1300	0.9128	0.2276	0.9283
Borderline-SMOTE + LSTM	0.8095	0.7987	0.8041	0.9021
Svm SMOTE + LSTM	0.7669	0.8389	0.8013	0.9073
SMOTEENN + LSTM	0.1367	0.8523	0.2372	0.9275
SMOTETomek + LSTM	0.1373	0.8725	0.2356	0.9387
kNN-SMOTE-LSTM (this work)	0.9496	0.8859	0.9167	0.9296

Note. “+” indicates the combination of models and “−” indicates the structural fusion of models.