Internet: saving private e-mail
IEEE Spectrum
Spam filters: bayes vs. chi-squared; letters vs. words
ISICT '03 Proceedings of the 1st international symposium on Information and communication technologies
Spam Filtering based on Preference Ranking
CIT '05 Proceedings of the The Fifth International Conference on Computer and Information Technology
Detecting Junk Mails by Implementing Statistical Theory
AINA '06 Proceedings of the 20th International Conference on Advanced Information Networking and Applications - Volume 02
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Spam and the ongoing battle for the inbox
Communications of the ACM - Spam and the ongoing battle for the inbox
Using word clusters to detect similar web documents
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
Identifying Spam Web Pages Based on Content Similarity
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
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Emails are one of the most commonly used modern communication media these days; however, unsolicited emails obstruct this otherwise fast and convenient technology for information exchange and jeopardize the continuity of this popular communication tool. Waste of valuable resources and time and exposure to offensive content are only a few of the problems that arise as a result of junk emails. In addition, the monetary cost of processing junk emails reaches billions of dollars per year and is absorbed by public users and Internet service providers. Even though there has been extensive work in the past dedicated to eradicate junk emails, none of the existing junk email detection approaches has been highly successful in solving these problems, since spammers have been able to infiltrate existing detection techniques. In this paper, we present a new tool, JunEX, which relies on the content similarity of emails to eradicate junk emails. JunEX compares each incoming email to a core of emails marked as junk by each individual user to identify unwanted emails while reducing the number of legitimate emails treated as junk, which is critical. Conducted experiments on JunEX verify its high accuracy.