Table 5.

Overall classification performance of the ML and DL models (all metrics are in %).

Model	Acc.	Prec.	Recall	F1 Score	FPR	NPV	AUC	GM	MCC	${\mathit{T}}_{\mathit{train}}$ (s)	${\mathit{T}}_{\mathit{test}}$ (s)
RNN [67]	99.91	-	-	-	1.28	-	-	-	-	201.70	44.23
SVM [67]	-	-	-	-	2.99	-	-	-	-	-	-
RF [67]	-	-	-	-	4.29	-	-	-	-	-	-
NB [67]	-	-	-	-	3.24	-	-	-	-	-	-
DNN [68]	98.22	-	-	-	1.14	-	-	-	-	991.60	-
RNN [68]	98.31	-	-	-	1.10	-	-	-	-	1400.60	-
CNN [68]	98.37	-	-	-	1.00	-	-	-	-	1367.20	-
RBM [68]	98.28	-	-	-	1.13	-	-	-	-	2111.90	-
DBN [68]	98.31	-	-	-	1.12	-	-	-	-	2921.70	-
DBM [68]	98.38	-	-	-	1.11	-	-	-	-	2800.10	-
DAE [68]	98.39	-	-	-	1.11	-	-	-	-	2816.20	-
RDTIDS [69]	97.00	-	-	-	1.12	-	-	-	-	195.50	2.27
BLSTM [70]	98.91	-	-	-	1.20	-	-	-	-	149.60	69.10
SMOTE-DRNN	100.00	99.50	99.75	99.62	0.00	100.00	99.87	99.74	99.62	1147.32	9.81