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Automatic Detection of Phishing URLs Using Machine Learning

Undergraduate #61
Discipline: Computer Sciences and Information Management
Subcategory: Computer Science & Information Systems

Richard North - Texas Southern University


Phishing attacks have resulted in an estimated $3.2 billion dollars worth of stolen property in 2007. The success rate for phishing attacks are increasing each year. Such unfortunate events are prime examples of why more things should be done in order to prevent scammers from victimizing more innocent people. Phishing attacks are becoming harder to detect and more elusive by using short time windows to launch attacks. This project focuses on URL phishing and the initiatives used to detect malicious URLs. In order to combat the increasing effectiveness of phishing attacks, we propose that combining statistical analysis of website URLs with machine learning techniques will give a more accurate classification of phishing URLs.

Funder Acknowledgement(s): National Science Foundation (NSF IIS-1359199)

Faculty Advisor: Rakesh Verma,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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