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Title

A Survey of Machine Learning Techniques for Spam Filtering

Author

Omar Saad, Aboul Ella Hassanien, Ashraf Darwish, Ramadan Faraj

Citation

Vol. 13  No. 1  pp. 103-110

Abstract

Email spam or junk e-mail (unwanted e-mail “usually of a commercial nature sent out in bulk”) is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is an important and popular one. Common uses for mail filters include organizing incoming email and removal of spam and computer viruses. A less common use is to inspect outgoing email at some companies to ensure that employees comply with appropriate laws. Users might also employ a mail filter to prioritize messages, and to sort them into folders based on subject matter or other criteria. Mail filters can be installed by the user, either as separate programs, or as part of their email program (email client). In email programs, users can make personal, ""manual"" filters that then automatically filter mail according to the chosen criteria. In this paper, we present a survey of the performance of five commonly used machine learning methods in spam filtering. Most email programs now also have an automatic spam filtering function.

Keywords

E-mail classification, Spam, Spam filtering, Machine learning, algorithms.

URL

http://paper.ijcsns.org/07_book/201301/20130117.pdf