Table 7 |
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|
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 |
|
|
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|
Ramanathan and Wechsler EURASIP Journal on Information Security 2012 2012:1 doi:10.1186/1687-417X-2012-1 |
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