Even as we struggle to deal with spam messages, researchers at the Concordia University have devised a new way to identify spam. This is a new statistical framework that can help users to filter spam quickly and efficiently. The new method has been identified after studies numerous old ones to block unwanted messages that we often receive on email.
“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,” researcher Ola Amayri has said.
While most of the research related to email span has so far has relied on automated extraction and analysis of the content in the mail, they have ignored the relevance of image-based content. According to the researchers at Concordia, spam is a combination of text and images content. “The majority of previous research has focused on the textual content of spam emails, ignoring visual content found in multimedia content, such as images. By considering patterns from text and images simultaneously, we’ve been able to propose a new method for filtering out spam,” Amayri added.
The researcher also added that new spam messages tend to use sophisticated tricks such as obscure text, words replaced with symbols and using the same images only morphed to an extent. Their new method uses techniques such as pattern recognition and data mining that have a better ability to check for unwanted spam emails.
Amayri added that the method is currently tested on English spam emails but it can be extended to other languages as well.
“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.