Table 7

phishGILLNET2--binary (phish versus not phish) classification performance

Topics

Weak learner for boosting

TPR

FPR

Precision

Recall

F-measure

ROC Area

Time (s)


50

C4.5

0.985

0.055

0.985

0.985

0.985

0.966

0.79

50

RIPPER

0.989

0.051

0.989

0.989

0.989

0.968

4.17

50

Random forest

0.993

0.053

0.993

0.993

0.993

0.999

1.31

50

SVM

0.939

0.355

0.935

0.939

0.937

0.792

12.67

50

Logistic

0.938

0.421

0.932

0.938

0.933

0.957

1.0

100

C4.5

0.995

0.02

0.995

0.995

0.995

0.987

1.58

100

RIPPER

0.997

0.012

0.997

0.997

0.997

0.993

6.82

100

Random forest

0.994

0.052

0.994

0.994

0.994

0.999

2.32

100

SVM

0.992

0.069

0.992

0.992

0.992

0.961

10.55

100

Logistic

0.995

0.023

0.995

0.995

0.995

0.994

2.17

200

C4.5

0.996

0.019

0.996

0.996

0.996

0.991

2.51

200

RIPPER

0.994

0.024

0.994

0.994

0.994

0.987

7.85

200

Random forest

0.995

0.037

0.995

0.995

0.995

0.999

2.87

200

SVM

0.988

0.098

0.988

0.988

0.988

0.945

10.78

200

Logistic

0.997

0.018

0.997

0.997

0.997

0.997

4.11


Ramanathan and Wechsler EURASIP Journal on Information Security 2012 2012:1   doi:10.1186/1687-417X-2012-1

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