Fast and Effective Clustering of Spam Emails Based on Structural Similarity

Spam emails yearly impose extremely heavy costs in terms of time, storage space and money to both private users and companies. Finding and persecuting spammers and eventual spam emails stakeholders should allow to directly tackle the root of the problem. To facilitate such a difficult analysis, which should be performed on large amounts of unclassified raw emails, in this paper we propose a framework to fast and effectively divide large amount of spam emails into homogeneous campaigns through structural similarity. The framework exploits a set of 21 features representative of the email structure and a novel categorical clustering algorithm named Categorical Clustering Tree (CCTree). The methodology is evaluated and validated through standard tests performed on three dataset accounting to more than 200k real recent spam emails.