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Digital Waste Sorting: A Goal-Based, Self-Learning Approach to Label Spam Email Campaigns

Fast analysis of correlated spam emails may be vital in the effort of finding and prosecuting spammers performing cybercrimes such as phishing and online frauds. This paper presents a self-learning framework to automatically divide and classify large amounts of spam emails in correlated labeled groups. Building on large datasets daily collected through honeypots, the emails are firstly divided into homogeneous groups of similar messages campaigns), which can be related to a specific spammer. Each campaign is then associated to a class which specifies the goal of the spammer, i.e. phishing, advertisement, etc. The proposed framework exploits a categorical clustering algorithm to group similar emails, and a classifier to subsequently label each email group. The main advantage of the proposed framework is that it can be used on large spam emails datasets, for which no prior knowledge is provided.  The approach has been tested on more than 3200 real and recent spam emails, divided in more than 60 campaigns, reporting a classification accuracy of 97\% on the classified data.


11th International Workshop on Security and Trust Management (STM2015), Vienna, Austria, 2015

Autori esterni: (), Mohamed Mejri (Universitè Laval), Nadia Tawbi (Universitè Laval)
Autori IIT:

Mina Sheikhalishahi

Foto di Mina Sheikhalishahi

Tipo: Articolo in Atti di convegno internazionale con referee
Area di disciplina: Computer Science & Engineering

File: stm15.pdf

Attività: Architetture, protocolli e meccanismi di sicurezza per sistemi e servizi distribuiti