TWC: Small: Unsupervised and Statistical Natural Language Processing Techniques for Automatic Phishing and Opinion Spam Detection

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In phishing, an attacker tries to steal sensitive information, e.g., bank/credit card account numbers, login information, etc., from Internet users. The US society and economy are increasingly dependent on the Internet and the web, which is plagued by phishing. One popular phishing method is to create a site that mimics a good site and then attract users to it via email, which is by far the most popular medium to entice unsuspecting users to the phishing site. Because of this modus operandi and the damages caused by phishing, it is important to design efficient and effective classifiers for emails and web sites. In this project, new techniques, inspired from natural language processing methods, are being designed for phishing email and web site detection. They are then implemented and validated rigorously on realistic data sets. They are also applied to automatic detection of opinion spam. Proposed research is expected to: (i) be useful in pushing the envelope of natural language processing techniques, and (ii) yield new applications of these techniques in cyber security. In the past, the PI has been very successful in involving women and minorities including underrepresented minorities in his research and this effort will be continued in this project. University of Houston has been recognized as a Hispanic-serving institution and the PI will continue his past successful efforts to involve underrepresented minorities including African Americans and Hispanics in this research. This research will be moved into the classroom and broadly disseminated through publications and software on the web.