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

Mina Sheikhalishahi, Andrea Saracino (Corresponding author), Fabio Martinelli, Antonio La Marra, Mohammed Mejri, Nadia Tawbi

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)499-522
Number of pages24
JournalInternational Journal of Information Security
Volume19
Issue number5
Early online date25 Sep 2019
DOIs
Publication statusPublished - 1 Oct 2020

Keywords

  • Classification
  • Clustering
  • Dynamic clustering
  • Spam campaign detection
  • Spam email

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