Network Traffic Anomaly Detection Model Based on Feature Reduction and Bidirectional LSTM Neural Network Optimization

Table 9

Detection results of the four models.

Dataset	Model	Evaluation metrics
		Accuracy (%)	Precision (%)	Recall (%)	F-score (%)
NSL-KDD	FR-APPSO-LSTM	90.02	83.33	95.26	88.89
	FR-BiLSTM	90.81	84.46	97.54	90.53
	APPSO-BiLSTM	90.40	83.86	95.97	89.61
	FR-APPSO-BiLSTM	91.76	85.37	98.50	91.46
UNSW-NB15	FR-APPSO-LSTM	85.19	92.97	92.26	92.61
	FR-BiLSTM	89.84	97.01	97.23	97.12
	APPSO-BiLSTM	91.41	96.92	97.00	97.06
	FR-APPSO-BiLSTM	92.08	97.88	98.32	98.10
CICIDS-2017	FR-APPSO-LSTM	92.29	86.15	87.58	86.86
	FR-BiLSTM	93.95	98.25	97.79	98.02
	APPSO-BiLSTM	94.20	98.02	97.73	98.12
	FR-APPSO-BiLSTM	95.44	98.58	98.40	98.49