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...

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Bibliographic Details
Published in:International journal of information security 2020-10, Vol.19 (5), p.499-522
Main Authors: Sheikhalishahi, Mina, Saracino, Andrea, Martinelli, Fabio, La Marra, Antonio, Mejri, Mohammed, Tawbi, Nadia
Format: Article
Language:English
Subjects:
Algorithms
Automation
Classification
Clustering
Coding and Information Theory
Communications Engineering
Computer Communication Networks
Computer privacy
Computer Science
Crime
Cryptology
Cybercrime
Data transmission
Information systems
Malware
Management of Computing and Information Systems
Network security
Networks
Operating Systems
Phishing
Protocol
Regular Contribution
Spamming
Studies
Waste disposal
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Summary: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.
ISSN:1615-5262
1615-5270
DOI:10.1007/s10207-019-00470-x