skip to main content
10.1145/2567948.2577320acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
poster

Users' behavioral prediction for phishing detection

Published: 07 April 2014 Publication History

Abstract

This study explores the users' web browsing behaviors that confront phishing situations for context-aware phishing detection. We extract discriminative features of each clicked URL, i.e., domain name, bag-of-words, generic Top-Level Domains, IP address, and port number, to develop a linear chain CRF model for users' behavioral prediction. Large-scale experiments show that our method achieves promising performance for predicting the phishing threats of users' next accesses. Error analysis indicates that our model results in a favorably low false positive rate. In practice, our solution is complementary to the existing anti-phishing techniques for cost-effectively blocking phishing threats from users' behavioral perspectives.

References

[1]
Lee, L.-H., Juan, Y.-C., Lee, K.-C., Tseng, W.-L., Chen, H.-H., and Tseng, Y.-H. 2012. Context-aware web security threat prevention. In Proceedings of CCS'12. 992--994.
[2]
Xiang, G. and Hong, J. 2009. A hybrid phish detection approach by identity discovery and keyword retrieval. In Proceedings of WWW'09, 571--580.
[3]
Zhang, Y., Hong, J. and Cranor, L. 2007. CANTINA: a content-based approach to detecting phishing web sites. In Proceedings of WWW'07, 639--648.

Cited By

View all
  • (2022)Machine Intelligence Based Web Page Phishing Detection2022 International Conference on Futuristic Technologies (INCOFT)10.1109/INCOFT55651.2022.10094411(1-5)Online publication date: 25-Nov-2022
  • (2022)Phishing Website Detection and ClassificationProceedings of International Conference on Deep Learning, Computing and Intelligence10.1007/978-981-16-5652-1_35(401-411)Online publication date: 27-Apr-2022
  • (2019)Phishing page detection via learning classifiers from page layout featureEURASIP Journal on Wireless Communications and Networking10.1186/s13638-019-1361-02019:1Online publication date: 20-Feb-2019
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web
April 2014
1396 pages
ISBN:9781450327459
DOI:10.1145/2567948
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

  • IW3C2: International World Wide Web Conference Committee

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

Check for updates

Author Tags

  1. behavioral analysis
  2. category prediction
  3. context-aware detection

Qualifiers

  • Poster

Conference

WWW '14
Sponsor:
  • IW3C2

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)17
  • Downloads (Last 6 weeks)2
Reflects downloads up to 20 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2022)Machine Intelligence Based Web Page Phishing Detection2022 International Conference on Futuristic Technologies (INCOFT)10.1109/INCOFT55651.2022.10094411(1-5)Online publication date: 25-Nov-2022
  • (2022)Phishing Website Detection and ClassificationProceedings of International Conference on Deep Learning, Computing and Intelligence10.1007/978-981-16-5652-1_35(401-411)Online publication date: 27-Apr-2022
  • (2019)Phishing page detection via learning classifiers from page layout featureEURASIP Journal on Wireless Communications and Networking10.1186/s13638-019-1361-02019:1Online publication date: 20-Feb-2019
  • (2018)Detecting Phishing Websites via Aggregation Analysis of Page LayoutsProcedia Computer Science10.1016/j.procs.2018.03.053129(224-230)Online publication date: 2018
  • (2017)Phishing DetectionSecurity and Communication Networks10.1155/2017/54210462017Online publication date: 10-Jan-2017
  • (2017)Phishing-Alarm: Robust and Efficient Phishing Detection via Page Component SimilarityIEEE Access10.1109/ACCESS.2017.27435285(17020-17030)Online publication date: 2017
  • (2017)Phishing Website Detection Based on Effective CSS Features of Web PagesWireless Algorithms, Systems, and Applications10.1007/978-3-319-60033-8_68(804-815)Online publication date: 27-May-2017
  • (2015)Why phishing still worksInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2015.05.00582:C(69-82)Online publication date: 1-Oct-2015
  • (2014)POSTERProceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security10.1145/2660267.2662362(1448-1450)Online publication date: 3-Nov-2014
  • (undefined)Phishing Websites Detection Using Machine LearningSSRN Electronic Journal10.2139/ssrn.4121102

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media