Please use this identifier to cite or link to this item: https://doi.org/10.1109/INCoS.2013.151
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dc.titleBaitAlarm: Detecting phishing sites using similarity in fundamental visual features
dc.contributor.authorMao, J.
dc.contributor.authorLi, P.
dc.contributor.authorLi, K.
dc.contributor.authorWei, T.
dc.contributor.authorLiang, Z.
dc.date.accessioned2014-07-04T03:11:43Z
dc.date.available2014-07-04T03:11:43Z
dc.date.issued2013
dc.identifier.citationMao, J.,Li, P.,Li, K.,Wei, T.,Liang, Z. (2013). BaitAlarm: Detecting phishing sites using similarity in fundamental visual features. Proceedings - 5th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2013 : 790-795. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/INCoS.2013.151" target="_blank">https://doi.org/10.1109/INCoS.2013.151</a>
dc.identifier.isbn9780769549880
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/78040
dc.description.abstractIn this paper, we present a new solution, BaitA-larm, to detect phishing attack using features that are hard to evade. The intuition of our approach is that phishing pages need to preserve the visual appearance the target pages. We present an algorithm to quantify the suspicious ratings of web pages based on similarity of visual appearance between the web pages. Since CSS is the standard technique to specify page layout, our solution uses the CSS as the basis for detecting visual similarities among web pages. We prototyped our approach as a Google Chrome extension and used it to rate the suspiciousness of web pages. The prototype shows the correctness and accuracy of our approach with a relatively low performance overhead. © 2013 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/INCoS.2013.151
dc.sourceScopus
dc.subjectAntiphishing
dc.subjectCSS
dc.subjectWeb security
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/INCoS.2013.151
dc.description.sourcetitleProceedings - 5th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2013
dc.description.page790-795
dc.identifier.isiutNOT_IN_WOS
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