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Detection of phishing webpages based on visual similarity

Published: 10 May 2005 Publication History

Abstract

An approach to detection of phishing webpages based on visual similarity is proposed, which can be utilized as a part of an enterprise solution for anti-phishing. A legitimate webpage owner can use this approach to search the Web for suspicious webpages which are visually similar to the true webpage. A webpage is reported as a phishing suspect if the visual similarity is higher than its corresponding preset threshold. Preliminary experiments show that the approach can successfully detect those phishing webpages for online use.

References

[1]
Anti-Phishing Working Group, http://www.antiphishing.org.
[2]
Chen Y., Ma W.Y., and Zhang H.J. Detecting webpage structure for adaptive viewing on small form factor devices. In Proceedings of the 12th International Conference on World Wide Web, pages 225-233, 2003.
[3]
Liu Y., Liu W., and Jiang C. User interest detection on webpages for building personalized information agent. In Proceedings of the Fifth International Conference on Web-Age Information Management (WAIM 2004), Dalian, China. LNCS, Vol. 3129, pages 280-287, 2004.
[4]
http://www.cs.cityu.edu.hk/~liuwy/phishing/testdata.zip

Cited By

View all
  • (2024)PhiSN: Phishing URL Detection Using Segmentation and NLP FeaturesJournal of Information Processing10.2197/ipsjjip.32.97332(973-989)Online publication date: 2024
  • (2024)STFL: Utilizing a Semi-Supervised, Transfer-Learning, Federated-Learning Approach to Detect Phishing URL Attacks2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650184(1-10)Online publication date: 30-Jun-2024
  • (2024)Phishing Website Detection Using Deep Learning ModelsIEEE Access10.1109/ACCESS.2024.348646212(167072-167087)Online publication date: 2024
  • Show More Cited By

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Reviews

Shannon Jacobs

Phishing is a label for certain strategies used by criminals to steal personal information via the Internet, and it is a rapidly growing problem. The classic approach involves using spam email containing "bait," typically some story about why the user should visit a Web site and "revalidate" their personal account information?while actually revealing that information in a counterfeit Web page created by the phisher. This report briefly summarizes three useful metrics the authors have evaluated for the detection of such counterfeit Web pages. The unstated premise is that this applies to phishers who are targeting "experienced" victims who have some familiarity with the authentic Web sites, so the visual similarity is critical to their phishing. (This premise is actually questionable, since experienced users are more likely to spot phishing attempts regardless of what the Web sites look like.) The main drawback of this report is that it doesn't present sufficiently detailed information about the results, but the limited space excuses that. A less serious (but less excusable) problem is that the English is significantly flawed, though most of the meaning can be teased out with a bit of interpretive effort. Online Computing Reviews Service

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cover image ACM Conferences
WWW '05: Special interest tracks and posters of the 14th international conference on World Wide Web
May 2005
454 pages
ISBN:1595930515
DOI:10.1145/1062745
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 May 2005

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Author Tags

  1. anti-phishing
  2. information filtering
  3. visual similarity
  4. web document analysis

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all
  • (2024)PhiSN: Phishing URL Detection Using Segmentation and NLP FeaturesJournal of Information Processing10.2197/ipsjjip.32.97332(973-989)Online publication date: 2024
  • (2024)STFL: Utilizing a Semi-Supervised, Transfer-Learning, Federated-Learning Approach to Detect Phishing URL Attacks2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650184(1-10)Online publication date: 30-Jun-2024
  • (2024)Phishing Website Detection Using Deep Learning ModelsIEEE Access10.1109/ACCESS.2024.348646212(167072-167087)Online publication date: 2024
  • (2024)An ensemble learning approach for detecting phishing URLs in encrypted TLS trafficTelecommunication Systems10.1007/s11235-024-01229-z87:4(1015-1031)Online publication date: 16-Oct-2024
  • (2023)UA-Radar: Exploring the Impact of User Agents on the WebProceedings of the 22nd Workshop on Privacy in the Electronic Society10.1145/3603216.3624958(31-43)Online publication date: 26-Nov-2023
  • (2023)Phishing Website Detection using Machine Learning Techniques2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP)10.1109/ICETET-SIP58143.2023.10151640(1-6)Online publication date: 28-Apr-2023
  • (2023)ORFPPrediction: Machine Learning Based Online Recruitment Fraud Probability Prediction2023 International Conference on the Cognitive Computing and Complex Data (ICCD)10.1109/ICCD59681.2023.10420822(139-144)Online publication date: 21-Oct-2023
  • (2023)Methods for Automatic Web Page Layout Testing and Analysis: A ReviewIEEE Access10.1109/ACCESS.2023.324254911(13948-13964)Online publication date: 2023
  • (2023)Identification of Phishing Attacks using Machine Learning AlgorithmE3S Web of Conferences10.1051/e3sconf/202339904010399(04010)Online publication date: 12-Jul-2023
  • (2023)Machine learning models for phishing detection from TLS trafficCluster Computing10.1007/s10586-023-04042-626:5(3263-3277)Online publication date: 30-May-2023
  • Show More Cited By

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