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Designing ethical phishing experiments: a study of (ROT13) rOnl query features
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Source International World Wide Web Conference archive
Proceedings of the 15th international conference on World Wide Web table of contents
Edinburgh, Scotland
SESSION: Security, privacy & ethics table of contents
Pages: 513 - 522  
Year of Publication: 2006
ISBN:1-59593-323-9
Authors
Markus Jakobsson  Indiana University, Bloomington, IN
Jacob Ratkiewicz  Indiana University, Bloomington, IN
Sponsors
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 13,   Downloads (12 Months): 107,   Citation Count: 7
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ABSTRACT

We study how to design experiments to measure the success rates of phishing attacks that are ethical and accurate, which are two requirements of contradictory forces. Namely, an ethical experiment must not expose the participants to any risk; it should be possible to locally verify by the participants or representatives thereof that this was the case. At the same time, an experiment is accurate if it is possible to argue why its success rate is not an upper or lower bound of that of a real attack -- this may be difficult if the ethics considerations make the user perception of the experiment different from the user perception of the attack. We introduce several experimental techniques allowing us to achieve a balance between these two requirements, and demonstrate how to apply these, using a context aware phishing experiment on a popular online auction site which we call "rOnl". Our experiments exhibit a measured average yield of 11% per collection of unique users. This study was authorized by the Human Subjects Committee at Indiana University (Study #05-10306).


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Mailfrontier phishing IQ test. http://survey.mailfrontier.com/survey/quiztest.html.
 
2
Know your enemy : Phishing. behind the scenes of phishing attacks. http://www.honeynet.org/papers/phishing/, 2005.
3
 
4
Jakobsson, M. Modeling and preventing phishing attacks. In Financial Cryptography (2005).
 
5
Lester, A. WWW::Mechanize - handy web browsing in a perl object. http://search.cpan.org/ petdance/WWW-Mechanize-1.16/lib/WWW/Mechanize.p%m, 2005.
 
6
Litan, A. Phishing attack victims likely targets for identity theft. FT-22-8873, Gartner Research (2004).
 
7
M. Jakobsson, T. Jagatic, S. S. Phishing for clues. www.browser-recon.info.
 
8
T. Jagatic, N. Johnson, M. J., and Menczer, F. Social phishing. 2006.

CITED BY  7
 
 
 

Collaborative Colleagues:
Markus Jakobsson: colleagues
Jacob Ratkiewicz: colleagues