PHISHING SITES DETECTION BASED ON URL CORRELATION

被引：0
|
作者：
Xue, Ying [1 ,2 ]
Li, Yang [1 ,2 ]
Yao, Yuangang [2 ]
Zhao, Xianghui [2 ]
Liu, Jianyi [1 ]
Zhang, Ru [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, Beijing 100876, Peoples R China
[2] China Informat Technol Secur Evaluat Ctr, Beijing 100085, Peoples R China
来源：
PROCEEDINGS OF 2016 4TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENCE SYSTEMS (IEEE CCIS 2016) | 2016年
关键词：
URL Correlation; Vulnerable Sites List; Weighting; Machine Learning Method;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With the rapid development of the information technology, internet security has drawn more and more attention, Nowadays, most researchers focus on lexical and host features to classify Phishing URLs. In this paper, we proposed Vulnerable Sites List and a new feature which is named URL Correlation. URL Correlation is based on the similarity of URLs with the List above that we created. In addition, a large improvement of accuracy is observed by comparing methods which use our new feature with the others which use the normal one.
引用
收藏
页码：244 / 248
页数：5
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