Researchers have proposed a new statistical framework for spam filtering that can quickly and efficiently block unwanted messages in your email inbox.
Scientists from the Concordia University have conducted a comprehensive study of several spam filters in the process of developing a new and efficient one.
"Our new method for spam filtering is able to adapt to the dynamic nature of spam emails and accurately handle spammers' tricks by carefully identifying informative patterns, which are automatically extracted from both text and images content of spam emails," said researcher Ola Amayri in a statement.
Until now, the majority of research in the domain of email spam filtering has focused on the automatic extraction and analysis of the textual content of spam emails and has ignored the rich nature of image-based content.
When these tricks are used in combination, traditional spam filters are powerless to stop the messages, because they normally focus on either text or images but rarely both, the study found.
"The majority of previous research has focused on the
Amayri explained that new spam messages often employ sophisticated tricks, such as deliberately obscuring text, obfuscating words with symbols, and using batches of the same images with different backgrounds and colours that might contain random text from the web.
By conducting extensive experiments on traditional spam filtering methods that were general and limited to patterns found in texts or images, the new method is much stronger, based on techniques used in pattern recognition and data mining, to filter out unwanted emails.
Although the new method has been tested on English spam emails, Amayri said it can be easily extended to other languages.
"Spammers keep adapting their methods so that they can trick the spam filters.
Researchers in this field need to work together to keep adapting our methods too, so that we can keep spam out and focus on those messages that are really important," Amayri added.