Table 2.

Performance evaluation results of anomaly detection using various Machine Learning Algorithms

K-Fold	Algorithm name	Precision	Recall	Accuracy	F1 Score
3-Fold	SVM	0.63	0.99	80.94	0.77
	Isolation forest	0.94	0.99	96.23	0.96
	Elliptic Envelope	0.94	0.94	93.83	0.94
	Local Outlier Factor	0.82	0.99	90.63	0.90
	k-means	0.94	0.99	96.43	0.96
	Mini Batch k-Means	0.94	0.99	96.43	0.96
	Mean Shift	0.61	0.64	63.30	0.63
	Birch	0.33	0.99	66.19	0.49
5-Fold	SVM	0.75	0.99	86.98	0.85
	Isolation forest	0.94	0.99	96.14	0.96
	Elliptic Envelope	0.94	0.90	91.81	0.92
	Local Outlier Factor	0.81	0.99	90.06	0.89
	k-means	0.94	0.99	96.43	0.96
	Mini Batch k-Means	0.94	0.99	96.43	0.96
	Mean Shift	0.61	0.64	63.47	0.63
	Birch	0.20	1.00	60.07	0.34
10-Fold	SVM	0.65	0.99	82.06	0.78
	Isolation forest	0.93	0.99	96.14	0.96
	Elliptic Envelope	0.94	0.86	89.54	0.90
	Local Outlier Factor	0.82	0.99	90.38	0.90
	k-means	0.94	0.99	96.43	0.96
	Mini Batch k-Means	0.95	0.72	79.44	0.82
	Mean Shift	0.61	0.64	63.64	0.63
	Birch	0.11	1.00	55.31	0.20