Please use this identifier to cite or link to this item:
https://doi.org/10.1016/j.procs.2018.03.053
DC Field | Value | |
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dc.title | Detecting Phishing Websites via Aggregation Analysis of Page Layouts | |
dc.contributor.author | Mao, J. | |
dc.contributor.author | Bian, J. | |
dc.contributor.author | Tian, W. | |
dc.contributor.author | Zhu, S. | |
dc.contributor.author | Wei, T. | |
dc.contributor.author | Li, A. | |
dc.contributor.author | Liang, Z. | |
dc.contributor.editor | Bie, R. | |
dc.contributor.editor | Sun, Y. | |
dc.contributor.editor | Yu, J. | |
dc.date.accessioned | 2021-11-16T09:30:40Z | |
dc.date.available | 2021-11-16T09:30:40Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Mao, J., Bian, J., Tian, W., Zhu, S., Wei, T., Li, A., Liang, Z. (2018). Detecting Phishing Websites via Aggregation Analysis of Page Layouts. Procedia Computer Science 129 : 224-230. ScholarBank@NUS Repository. https://doi.org/10.1016/j.procs.2018.03.053 | |
dc.identifier.issn | 1877-0509 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/206488 | |
dc.description.abstract | Phishing websites are typical starting points of online social engineering attacks, including many recent online scams. The attackers develop web pages mimicking legitimate websites, and send the malicious URLs to victims to lure them to input their sensitive information. Existing phishing defense mechanisms are not sufficient to detect with new phishing attacks. In this paper, we aim to improve phishing detection techniques using machine learning techniques. In particular, we propose a learning-based aggregation analysis mechanism to decide page layout similarity, which is used to detect phishing pages. Our experiment results shows that our approach is accurate and effective in detecting phishing pages. © 2018 Elsevier Ltd. All rights reserved. | |
dc.publisher | Elsevier B.V. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.source | Scopus OA2018 | |
dc.subject | Aggregation analysis | |
dc.subject | Anti-phishing | |
dc.subject | Machine learning | |
dc.subject | Web security | |
dc.type | Conference Paper | |
dc.contributor.department | DEPARTMENT OF COMPUTER SCIENCE | |
dc.description.doi | 10.1016/j.procs.2018.03.053 | |
dc.description.sourcetitle | Procedia Computer Science | |
dc.description.volume | 129 | |
dc.description.page | 224-230 | |
Appears in Collections: | Staff Publications Elements |
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