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中国矿业大学计算机科学与技术学院,江苏 徐州 221116
[ "韩浩(1993-),男,中国矿业大学计算机科学与技术学院硕士生,主要研究方向为信息安全。" ]
[ "刘博文(1993-),男,中国矿业大学计算机科学与技术学院硕士生,主要研究方向为云计算和信息安全。" ]
[ "林果园(1975-),男,博士,中国矿业大学计算机科学与技术学院副教授、硕士生导师、信息安全系主任、信息安全竞赛指导委员会副主任,主要研究方向为网络空间安全、移动互联及其安全、云计算及其安全、信息系统及其安全、基于位置服务、矿井信息化等。" ]
网络出版日期:2018-03,
纸质出版日期:2018-03-20
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韩浩, 刘博文, 林果园. 基于改进的TrustRank算法的钓鱼网站检测[J]. 电信科学, 2018,34(3):86-94.
Hao HAN, Bowen LIU, Guoyuan LIN. Detection of phishing websites based on the improved TrustRank algorithm[J]. Telecommunications science, 2018, 34(3): 86-94.
韩浩, 刘博文, 林果园. 基于改进的TrustRank算法的钓鱼网站检测[J]. 电信科学, 2018,34(3):86-94. DOI: 10.11959/j.issn.1000-0801.2018046.
Hao HAN, Bowen LIU, Guoyuan LIN. Detection of phishing websites based on the improved TrustRank algorithm[J]. Telecommunications science, 2018, 34(3): 86-94. DOI: 10.11959/j.issn.1000-0801.2018046.
钓鱼检测方式一般只是通过比较网页间特征的相似度判断钓鱼网站,容易被攻击者根据特征提取过程反检测。因此,提出依据网页之间的关系来检测钓鱼网站,通过设立网页集合、结合钓鱼网站与其他网站间的链接关系改进TrustRank算法来检测钓鱼网站。实验证明,改进的TrustRank算法可以将钓鱼网站的信任值集中在一个范围内,并且与其他方法相比具有较低的误判率、漏判率和较高的速度,可以很好地检测钓鱼网站。
Anti-phishing methods just generally detect phishing sites based on comparing the similarity of pages features
which could be anti-detected when attackers are clear about the extraction process of features.Therefore
a method based on web pages relationship was proposed to detect phishing websites.According to this method
phishing websites were detected by an improved TrustRank algorithm which set up a collection of web pages and used the link relationship between phishing websites and other websites.Experiments show that the improved TrustRank algorithm can focus the trust values of phishing websites on some range with the lower false positive rate
the lower false negative rate and the higher speed compared to other methods.
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CNNIC . Statistical report on current situation of global chinese fishing website(2016) [R/OL ] . ( 2017 - 10 - 29 ).[ 2017 - 01 - 07 ] . http://www.sohu.com/a/14765781-352856 http://www.sohu.com/a/14765781-352856 .
SONOWAL G , KUPPUSAMY K S . PhiDMA-A phishing detection model with multi-filter approach [J ] . Journal of King Saud University-Computer and Information Sciences , 2017 .
MOGHIMI M , VARJANI A Y . New rule-based phishing detection method [J ] . Expert Systems with Applications , 2016 ( 53 ): 231 - 242 .
TAN C L , KANG L C , WONG K S , et al . PhishWHO:phishing webpage detection via identity keywords extraction and target domain name finder [J ] . Decision Support Systems , 2016 ( 88 ): 18 - 27 .
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RAJAB K D . New hybrid features selection method:a case study on websites phishing [J ] . Security & Communication Networks , 2017 ( 2 ): 1 - 10 .
HADI W , ABURUB F , ALHAWARI S . A new fast associative classification algorithm for detecting phishing websites [M ] . Netherlands : Elsevier Science Publishers 2016 .
RATHORE S , SHARMA P K , LOIA V , et al . Social network security:issues,challenges,threats,and solutions [J ] . Information Sciences , 2017 .
SOUSA J , RIBEIRO L , MARQUES A S , et al . Locating leaks with trustrank algorithm support [J ] . Water , 2015 , 7 ( 4 ): 1378 - 1401 .
刘阳 , 张化祥 . 基于结合内容特征的 TrustRank 算法改进 [J ] . 计算机工程与设计 , 2013 , 34 ( 4 ): 1276 - 1279 .
LIU Y , ZHANG H X . Improvement of TrustRank algorithm based on combination of content features [J ] . Computer Engineering and Design , 2013 , 34 ( 4 ): 1276 - 1279 .
ZOU F , GANG Y , PEI B , et al . Web Phishing detection based on graph mining [C ] // IEEE International Conference on Computer and Communications , Jul 1 - 2 , 2017 , Japur,India.Piscataway : IEEE Press , 2017 .
宋明秋 , 曹晓芸 . 基于敏感特征的网络钓鱼网站检测方法 [J ] . 大连理工大学学报 , 2013 , 53 ( 6 ): 903 - 907 .
SONG M Q , CAO X Y . Detection method of phishing site based on sensitive feature [J ] . Journal of Dalian Unversity of Technology , 2013 , 53 ( 6 ): 903 - 907 .
ZHANG J , PAN Y , WANG Z , et al . URL based gateway side phishing detection method [C ] // Trustcom/bigdatase/ispa , Aug 1 - 4 , 2017 , Sydney,Australia.Piscataway : IEEE Press , 2017 : 268 - 275 .
XUE Y , LI Y , YAO Y , et al . Phishing sites detection based on Url Correlation [C ] // International Conference on Cloud Computing and Intelligence Systems , 2016 , Beijing,China.New Piscataway : IEEE Press , 2016 : 244 - 248 .
GYONGYI Z , GARCIA-M H . Seed selection in TrustRank.Technical report [R ] . Stanford University , 2004 .
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