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Guang Xiang


Publications by Guang Xiang (bibliography)

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Liu, Gang, Xiang, Guang, Pendleton, Bryan A., Hong, Jason I. and Liu, Wenyin (2011): Smartening the crowds: computational techniques for improving human verification to fight phishing scams. In: Proceedings of the 2011 Symposium on Usable Privacy and Security 2011. p. 8. http://dx.doi.org/10.1145/2078827.2078838

Phishing is an ongoing kind of semantic attack that tricks victims into inadvertently sharing sensitive information. In this paper, we explore novel techniques for combating the phishing problem using computational techniques to improve human effort. Using tasks posted to the Amazon Mechanical Turk human effort market, we measure the accuracy of minimally trained humans in identifying potential phish, and consider methods for best taking advantage of individual contributions. Furthermore, we present our experiments using clustering techniques and vote weighting to improve the results of human effort in fighting phishing. We found that these techniques could increase coverage over and were significantly faster than existing blacklists used today.

© All rights reserved Liu et al. and/or ACM Press

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Lin, Jialiu, Xiang, Guang, Hong, Jason I. and Sadeh, Norman (2010): Modeling people's place naming preferences in location sharing. In: Proceedings of the 2010 International Conference on Uniquitous Computing 2010. pp. 75-84. http://doi.acm.org/10.1145/1864349.1864362

Most location sharing applications display people's locations on a map. However, people use a rich variety of terms to refer to their locations, such as "home," "Starbucks," or "the bus stop near my house." Our long-term goal is to create a system that can automatically generate appropriate place names based on real-time context and user preferences. As a first step, we analyze data from a two-week study involving 26 participants in two different cities, focusing on how people refer to places in location sharing. We derive a taxonomy of different place naming methods, and show that factors such as a person's perceived familiarity with a place and the entropy of that place (i.e. the variety of people who visit it) strongly influence the way people refer to it when interacting with others. We also present a machine learning model for predicting how people name places. Using our data, this model is able to predict the place naming method people choose with an average accuracy higher than 85%.

© All rights reserved Lin et al. and/or their publisher

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Xiang, Guang and Hong, Jason I. (2009): A hybrid phish detection approach by identity discovery and keywords retrieval. In: Proceedings of the 2009 International Conference on the World Wide Web 2009. pp. 571-580. http://doi.acm.org/10.1145/1526709.1526786

Phishing is a significant security threat to the Internet, which causes tremendous economic loss every year. In this paper, we proposed a novel hybrid phish detection method based on information extraction (IE) and information retrieval (IR) techniques. The identity-based component of our method detects phishing webpages by directly discovering the inconsistency between their identity and the identity they are imitating. The keywords-retrieval component utilizes IR algorithms exploiting the power of search engines to identify phish. Our method requires no training data, no prior knowledge of phishing signatures and specific implementations, and thus is able to adapt quickly to constantly appearing new phishing patterns. Comprehensive experiments over a diverse spectrum of data sources with 11449 pages show that both components have a low false positive rate and the stacked approach achieves a true positive rate of 90.06% with a false positive rate of 1.95%.

© All rights reserved Xiang and Hong and/or ACM Press

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