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Digital Waste Disposal: an automated framework for analysis of spam emails.

Spam email automated analysis and classification are a challenging task, which is vital in the identification of botnet structures and cybercrime fighting. In this work, we propose an automated methodology and the resulting framework based on innovative categorical divisive clustering, used both for grouping and for classification of spam messages. In particular, the grouping is exploited to identify campaigns of similar spam emails, while the classification is used to label specific emails according to the goal of spammer (e.g., phishing, malware distribution, advertisement, etc.). This work introduces the CCTree algorithm, both as clustering algorithm and as classification algorithm, in two operative modes: batch and dynamic, to handle both large data sets and data streams. Afterward, the CCTree is applied to large sets of spam emails for campaign identification and labeling. The performance of the algorithm is reported for both clustering and classification, and a comparison between the batch and dynamic approaches is presented and discussed.

International Journal of Information Security, 2020

Autori esterni: Mohammed Mejri (University of Quebec), Nadia Tawbi (University of Quebec)
Autori IIT:

Antonio La Marra

Foto di Antonio La Marra

Mina Sheikhalishahi

Foto di Mina Sheikhalishahi

Tipo: Contributo in rivista ISI
Area di disciplina: Computer Science & Engineering

File: main.pdf

Attività: Sicurezza di dispositivi mobili