EURASIP Journal on Information Security

Table 8 phishGILLNET3--binary (phish versus not phish) classification performance

From: phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training

Iteration number	TPR	FPR	Precision	Recall	F-measure	ROC area
5	0.997	0.014	0.997	0.997	0.997	0.987
10	0.998	0.015	0.998	0.998	0.998	0.99
15	0.999	0.014	0.999	0.999	0.999	0.989
20	1.0	0.012	1.0	1.0	1.0	0.991
25	1.0	0.012	1.0	1.0	1.0	0.991
30	1.0	0.013	1.0	1.0	1.0	0.991
35	1.0	0.015	1.0	1.0	1.0	0.991
40	1.0	0.009	1.0	1.0	1.0	0.993
45	1.0	0.0009	1.0	1.0	1.0	0.999

(PLSA 200 topics + AdaBoost with logistic regression weak learner + Co-Training)

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