Users' behavioral prediction for phishing detection

Lung Hao Lee, Kuei Ching Lee, Yen Cheng Juan, Hsin Hsi Chen, Yuen Hsien Tseng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages337-338
Number of pages2
ISBN (Electronic)9781450327459
DOIs
Publication statusPublished - 2014 Apr 7
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: 2014 Apr 72014 Apr 11

Publication series

NameWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web

Other

Other23rd International Conference on World Wide Web, WWW 2014
CountryKorea, Republic of
CitySeoul
Period14/4/714/4/11

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Keywords

  • Behavioral analysis
  • Category prediction
  • Context-aware detection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Lee, L. H., Lee, K. C., Juan, Y. C., Chen, H. H., & Tseng, Y. H. (2014). Users' behavioral prediction for phishing detection. In WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web (pp. 337-338). (WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web). Association for Computing Machinery, Inc. https://doi.org/10.1145/2567948.2577320