Table 11.

Machine learning algorithms and methods employed in the selected primary studies

Category	Method	Publications	Learning task
ARIMA models	ARIMA	[48, 61]	unsupervised
Partition-based algorithms	MCOD	[69]	unsupervised
Decision Trees	Adaboost	[72]	supervised
	CART	[41, 46, 68]	supervised
	IF	[67]	supervised
	RF	[43, 44, 46, 52, 53]	supervised
	RSF	[71]	unsupervised
	GBDT	[76]	supervised
Dynamic Bayes Networks	DBF	[63]	supervised
	HMM	[42]	unsupervised
Hybrid models	ARIMA + LSTM	[50]	unsupervised
	DBSCAN + RF	[74]	unsupervised + supervised
	DBSCAN + SVM	[70]	unsupervised + supervised
	[HC / time series clustering] + RNN	[59]	unsupervised + supervised
	One-class SVM + K-Means + RF	[45]	semi-supervised + unsupervised + supervised
	Autoencoder + Simple Linear Regression	[58]	unsupervised + supervised
	GMM + FP-Growth + CBA-CB	[75]	unsupervised + supervised
	CNN + NCA + Medium Gaussian SVM / CNN + NCA + ensemble subspace K-NN	[80]	supervised
Instance-based algorithms	K-NN	[39]	supervised
Latent Variable Models	PCA	[65]	unsupervised
	GMM	[47]	unsupervised
	K-Means	[54]	unsupervised
	PLSR	[64]	supervised
	K-SVD	[60]	unsupervised
	K-MDTSC	[62]	unsupervised
Artificial Neural Networks	ANN	[57]	supervised
	BPNN	[40]	supervised
	CNN	[78]	supervised
	DNN	[77]	supervised
	LSTM	[70]	supervised
	MLP	[56]	supervised
	SSAE + BPNN	[31]	unsupervised + supervised
	SSAE + Softmax Classifier	[81]	unsupervised + supervised
	LSTM Autoencoder	[73]	supervised
	LSTM - GAN	[79]	supervised
	RNN	[55]	supervised
	Conditional Variational Autoencoder	[66]	unsupervised
Rule-based models	R4RE (“Rules 4 Rare Events” based on QARMA)	[49]	supervised
	XCSR	[51]	supervised