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

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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.

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  1. http://www.cavar.me/damir/LID/.

  2. http://mathworks.com.

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Funding

This study was funded by H2020 C3ISP Project (GA 700294).

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Mina Sheikhalishahi

  2. Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, Pisa, Italy

    Andrea Saracino, Fabio Martinelli & Antonio La Marra

  3. Univesity of Lavalle, Quebec City, QC, Canada

    Mohammed Mejri & Nadia Tawbi

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  1. Mina Sheikhalishahi
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  2. Andrea Saracino
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  3. Fabio Martinelli
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Correspondence to Andrea Saracino.

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Sheikhalishahi, M., Saracino, A., Martinelli, F. et al. Digital Waste Disposal: an automated framework for analysis of spam emails. Int. J. Inf. Secur. 19, 499–522 (2020). https://doi.org/10.1007/s10207-019-00470-x

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