DataAnalysisPhishingwebsitedetection.docx

My dataset- Dataset -

My project -

Data Analysis Phishing website detection

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Fig 1 Cloud for words used in good url and fake website url

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Fig 2 Domains of fake and genuine website

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Table 1: Accuracies of Different Machine Learning Models

Training %

Logistic

Regression

Random Forest

Decision Tree

KNN

SVM

LSVC

NB

10

0.83

0.83

0.82

0.74

0.78

0.87

0.86

20

0.86

0.87

0.85

0.73

0.83

0.88

0.88

30

0.88

0.88

0.85

0.73

0.85

0.90

0.88

40

0.88

0.88

0.85

0.74

0.86

0.90

0.89

50

0.89

0.89

0.86

0.75

0.86

0.91

0.90

60

0.90

0.89

0.87

0.75

0.87

0.91

0.90

70

0.90

0.90

0.87

0.76

0.88

0.91

0.91

80

0.90

0.90

0.87

0.74

0.88

0.91

0.91

90

0.90

0.89

0.88

0.74

0.87

0.90

0.90

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Table 2: Precisions of Different Machine Learning Models

Training %

Logistic

Regression

Random Forest

Decision Tree

KNN

SVM

LSVC

NB

10

0.91

0.89

0.78

0.56

0.95

0.91

0.96

20

0.91

0.91

0.84

0.53

0.92

0.89

0.94

30

0.92

0.93

0.82

0.52

0.92

0.90

0.93

40

0.90

0.92

0.81

0.54

0.89

0.89

0.91

50

0.89

0.94

0.81

0.56

0.89

0.90

0.92

60

0.92

0.93

0.82

0.56

0.94

0.91

0.92

70

0.93

0.93

0.83

0.59

0.94

0.91

0.90

80

0.92

0.93

0.82

0.55

0.93

0.90

0.89

90

0.93

0.94

0.85

0.54

0.92

0.92

0.89

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Table 3: Recalls of Different Machine Learning Models

Training %

Logistic

Regression

Random Forest

Decision Tree

KNN

SVM

LSVM

NB

10

0.45

0.49

0.56

0.46

0.26

0.62

0.52

20

0.58

0.60

0.60

0.53

0.45

0.67

0.60

30

0.64

0.64

0.61

0.59

0.51

0.70

0.65

40

0.68

0.65

0.67

0.57

0.57

0.75

0.70

50

0.71

0.70

0.68

0.59

0.60

0.76

0.72

60

0.72

0.68

0.69

0.60

0.59

0.75

0.73

70

0.74

0.70

0.70

0.60

0.63

0.78

0.77

80

0.72

0.70

0.74

0.61

0.62

0.75

0.78

90

0.69

0.67

0.69

0.62

0.60

0.71

0.74

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