@inproceedings{5e33f775768b489fbd85b374d283dd97,
title = "Users' behavioral prediction for phishing detection",
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.",
keywords = "Behavioral analysis, Category prediction, Context-aware detection",
author = "Lee, {Lung Hao} and Lee, {Kuei Ching} and Juan, {Yen Cheng} and Chen, {Hsin Hsi} and Tseng, {Yuen Hsien}",
note = "Publisher Copyright: {\textcopyright} Copyright 2014 by the International World Wide Web Conferences Steering Committee.; 23rd International Conference on World Wide Web, WWW 2014 ; Conference date: 07-04-2014 Through 11-04-2014",
year = "2014",
month = apr,
day = "7",
doi = "10.1145/2567948.2577320",
language = "???core.languages.en_GB???",
series = "WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web",
publisher = "Association for Computing Machinery, Inc",
pages = "337--338",
booktitle = "WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web",
}