Journal
INFORMATION PROCESSING & MANAGEMENT
Volume 54, Issue 2, Pages 303-317Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2017.12.001
Keywords
Genetic programing; Regular expressions; Automatic generation; E-mail; Spam filtering
Funding
- Xunta de Galicia
- Conselleria de Cultura, Educacion e Ordenacion Universitaria (Xunta de Galicia)
- FEDER (European Union)
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One of the most relevant problems affecting the efficient use of e-mail to communicate worldwide is the spam phenomenon. Spamming involves flooding Internet with undesired messages aimed to promote illegal or low value products and services. Beyond the existence of different well-known machine learning techniques, collaborative schemes and other complementary approaches, some popular anti-spam frameworks such as SpamAssassin or Wirebrush4SPAM enabled the possibility of using regular expressions to effectively improve filter performance. In this work, we provide a review of existing proposals to automatically generate fully functional regular expressions from any input dataset combining spam and ham messages. Due to configuration difficulties and the low performance achieved by analysed schemes, in this work we introduce DiscoverRegex, a novel automatic spam pattern-finding tool. Patterns generated DiscoverRegex outperform those created by existing approaches (able to avoid FP errors) whilst minimising the computational resources required for its proper operation. DiscoverRegex source code is publicly available at https://github.com/sing-group/DiscoverRegex.
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