Phishing and Other Fake Websites

The following papers on phishing, fake websites, and other related topics. See the website at for PDF links to these and other papers:

  • Zahedi, F. M., Abbasi, A., and Chen, Y. “Fake-Website Detection Tools: Identifying Design Elements that Promote Individuals’ Use and Enhance their Performance,” Journal of the Association for Information Systems, forthcoming.
  • Abbasi, A., Zahedi, F. M., Zeng, D., Chen, Y., Chen, H., and Nunamaker Jr., J. F. “Enhancing Predictive Analytics for Anti-Phishing by Exploiting Website Genre Information,” Journal of Management Information Systems, forthcoming.
  • Abbasi, A., Albrecht, C. C., Vance, A., and Hansen, J. V. “MetaFraud: A Meta-learning Framework for Detecting Financial Fraud,” MIS Quarterly, 36(4), 2012, pp. 1293-1397.
  • Abbasi, A., Zahedi, F. M., and Kaza, S. “Detecting Fake Medical Websites using Recursive Trust Labeling,” ACM Transactions on Information Systems, 30(4), 2012, no. 22.
  • Abbasi, A., Zhang, Z., Zimbra, D., Chen, H., and Nunamaker Jr., J. F. “Detecting Fake Websites: The Contribution of Statistical Learning Theory,” MIS Quarterly, 34(3), 2010, pp. 435-461 (MISQ Best Paper Award for 2010).
  • Abbasi, A. and Chen, H. “A Comparison of Tools for Detecting Fake Websites,” IEEE Computer, 42(10), 2009, pp. 78-86.
  • Abbasi, A. and Chen, H. “A Comparison of Fraud Cues and Classification Methods for Fake Escrow Website Detection,” Information Technology and Management, 10(2), 2009, pp. 83-101.



Intelligence and Security Informatics Data Sets

Data Infrastructure Building Blocks for ISI. A Project of the University of Arizona (NSF #ACI-1443019), Drexel University,

University of Virginia, University of Texas at Dallas, and University of Utah

The following are a few of the recent papers published using data sets collected for the Dark Web, Hacker Web, and other AI Lab projects (prior to the construction of the AZSecure-data demonstration site).

Have you written a paper using a data set provided here?  Please send us the citation for your published work and we will include it on the website.

Dark Web Forums

  • W. Li and H. Chen. Identifying Top Sellers In Underground Economy Using Deep Learning-based Sentiment Analysis. IEEE International Conference on Intelligence and Security Informatics, 2015. [Hacker Web, NSF SES-1314631 and DUE-1303362]
  • V. Benjamin, D. Zimbra, and H. Chen, “Bridging the Virtual and Real: The Relationships between Web Content, Linkage, and Geographical Proximity of Social Movements,” Journal of the American Society for Information Science and Technology, forthcoming, 2014.  [Dark Web and GeoWeb, DTRA HDTRA-09-0058]
  • Y. Zhang, Y. Dang, and H. Chen, Research note: Examining gender emotional differences in Web forum communication.  Decision Support Systems, 55(3), 2013 [Dark Web, NSF CNS-0709338]
  • T. Fu, A. Abbasi, D. Zeng, and H. Chen, Sentimental Spidering: Leveraging Opinion Information in Focused Crawlers. ACM Transactions on Information Systems, 30(4), 2012. [Dark Web project, DTRA HDTRA-09-0058), and NSF: CNS- 0709338, CBET-0730908, IIS-1236970]
  • A. Abbasi and H. Chen, “CyberGate: A System and Design for Text Analysis of Computer Mediated
    Communications,” MIS Quarterly, Volume 32, Number 4, Pages 811-837, December 2008.

  • A. Abbasi, H. Chen, S. Thoms, and T. J. Fu, “Affect Analysis of Web Forums and Blogs using Correlation Ensembles,” IEEE Transactions on Knowledge and Data Engineering, 20(8), pp. 1168-1180, September 2008.
  • J. Qin, Y. Zhou, E. Reid, G. Lai, and H. Chen, “Analyzing Terror Campaign on the Internet: Technical Sophistication, Content Richness, and Web Interactivity,” International Journal of Human-Computer Studies, special issue on Information Security in the Knowledge Economy, v. 65, pp. 71-84, 2007.

Other Work and Papers by the Artificial Intelligence Lab

Citations to all papers by the Artificial Intelligence Lab can be found on the AI Lab website at[NOTE: The AI Lab website is offline but will be available soon.]

Here are links to other related research and projects by the Artificial Intelligence Lab: