{"title":"Beyond the lock icon: real-time detection of phishing websites using public key certificates","authors":"Zheng Dong, Apu Kapadia, J. Blythe, L. Camp","doi":"10.1109/ECRIME.2015.7120795","DOIUrl":null,"url":null,"abstract":"We propose a machine-learning approach to detect phishing websites using features from their X.509 public key certificates. We show that its efficacy extends beyond HTTPS-enabled sites. Our solution enables immediate local identification of phishing sites. As such, this serves as an important complement to the existing server-based anti-phishing mechanisms which predominately use blacklists. Blacklisting suffers from several inherent drawbacks in terms of correctness, timeliness, and completeness. Due to the potentially significant lag prior to site blacklisting, there is a window of opportunity for attackers. Other local client-side phishing detection approaches also exist, but primarily rely on page content or URLs, which are arguably easier to manipulate by attackers. We illustrate that our certificate-based approach greatly increases the difficulty of masquerading undetected for phishers, with single millisecond delays for users. We further show that this approach works not only against HTTPS-enabled phishing attacks, but also detects HTTP phishing attacks with port 443 enabled.","PeriodicalId":127631,"journal":{"name":"2015 APWG Symposium on Electronic Crime Research (eCrime)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"50","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 APWG Symposium on Electronic Crime Research (eCrime)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECRIME.2015.7120795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 50
Abstract
We propose a machine-learning approach to detect phishing websites using features from their X.509 public key certificates. We show that its efficacy extends beyond HTTPS-enabled sites. Our solution enables immediate local identification of phishing sites. As such, this serves as an important complement to the existing server-based anti-phishing mechanisms which predominately use blacklists. Blacklisting suffers from several inherent drawbacks in terms of correctness, timeliness, and completeness. Due to the potentially significant lag prior to site blacklisting, there is a window of opportunity for attackers. Other local client-side phishing detection approaches also exist, but primarily rely on page content or URLs, which are arguably easier to manipulate by attackers. We illustrate that our certificate-based approach greatly increases the difficulty of masquerading undetected for phishers, with single millisecond delays for users. We further show that this approach works not only against HTTPS-enabled phishing attacks, but also detects HTTP phishing attacks with port 443 enabled.