An efficient method for filtering image-based spam e-mail
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
A survey of emerging approaches to spam filtering
ACM Computing Surveys (CSUR)
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In this paper, a fuzzy-matching clustering algorithm is introduced to group subjects found in spam emails which are generated by malware. A modified scoring strategy is applied in dynamic programming to find subjects that are similar to each other. A recursive seed selection strategy allows the algorithm to detect similar patterns even when the spammer creates a variation of the original pattern. A sliding threshold based on string length helps to minimize false-positives. The algorithm proves to be effective in detecting and grouping spam emails using templates. It also helps spam investigators to collect and sort large amount of malware-generated spam more efficiently without looking at the email content.