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Advances, Systems and Applications

Table 2 Performance evaluation results of anomaly detection using various Machine Learning Algorithms

From: Deep learning approach to security enforcement in cloud workflow orchestration

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