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Data Analysis and Knowledge Discovery  2017, Vol. 1 Issue (6): 47-55    DOI: 10.11925/infotech.2096-3467.2017.06.05
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Identifying Phishing Websites with Multiple Online Data Sources
Hu Zhongyi(), Wang Chaoqun, Wu Jiang
School of Information Management, Wuhan University, Wuhan 430072, China
The Center for Electronic Commerce Research and Development, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This study aims to identify phishing websites more effectively with the help of online evaluation data and URL abnormal features. [Methods] First, we used eight machine learning techniques to compare the performance of various online evaluation data and URL abnormal features in identifying phishing websites. Then, we proposed a new method to improve the accuracy of the identification procedures. [Results] We found that the evaluation data had better performance than abnormal features of URL. Combining the two data sets could improve the identification performance. [Limitations] We did not consider the difference between the numbers of phishing sites and the good ones. [Conclusions] Online evaluation data and URL abnormal features could help us identify phishing websites effectively, which indicates the direction of future studies.

Key wordsData Mining      Phishing Websites Identification      Machine Learning     
Received: 10 April 2017      Published: 25 August 2017
ZTFLH:  G353  

Cite this article:

Hu Zhongyi,Wang Chaoqun,Wu Jiang. Identifying Phishing Websites with Multiple Online Data Sources. Data Analysis and Knowledge Discovery, 2017, 1(6): 47-55.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2017.06.05     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2017/V1/I6/47

判断是正常网站 判断是钓鱼网站
实际是正常网站 TN FP
实际是钓鱼网站 FN TP
方法 准确率 查准率 查全率 F值
决策树 0.5935 0.9099 0.2150 0.3433
SVM 0.6340 0.7744 0.3780 0.5074
K近邻法 0.6205 0.6411 0.5610 0.5954
朴素贝叶斯 0.5990 0.9720 0.2040 0.3362
人工神经网络 0.6420 0.7535 0.4290 0.5457
AdaBoost 0.6435 0.7500 0.4400 0.5534
Bagging 0.6445 0.7587 0.4260 0.5443
随机森林 0.6390 0.7828 0.3850 0.5155
方法 准确率 查准率 查全率 F值
决策树 0.8810 0.8576 0.9160 0.8845
SVM 0.9145 0.9026 0.9310 0.9159
K近邻法 0.9115 0.9030 0.9240 0.9126
朴素贝叶斯 0.7455 0.6659 0.9890 0.7956
人工神经网络 0.8695 0.9226 0.8460 0.8818
AdaBoost 0.9415 0.9335 0.9500 0.9412
Bagging 0.9230 0.9174 0.9310 0.9234
随机森林 0.9415 0.9355 0.9500 0.9421
方法 准确率 查准率 查全率 F值
决策树 0.8810 0.8576 0.9160 0.8845
SVM 0.9119 0.9280 0.9194 0.9185
K近邻法 0.9200 0.9133 0.9300 0.9208
朴素贝叶斯 0.7690 0.6881 0.9880 0.8108
人工神经网络 0.8945 0.8879 0.8710 0.8776
AdaBoost 0.9415 0.9383 0.9430 0.9403
Bagging 0.9230 0.9174 0.9310 0.9234
随机森林 0.9435 0.9363 0.9530 0.9442
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