Classification of Spam Emails through Hierarchical Clustering and Supervised Learning

• URL: http://arxiv.org/abs/2005.08773v2
• Date: Thu, 28 May 2020 15:36:25 GMT
• Title: Classification of Spam Emails through Hierarchical Clustering and Supervised Learning
• Authors: Francisco J\'a\~nez-Martino, Eduardo Fidalgo, Santiago Gonz\'alez-Mart\'inez, Javier Velasco-Mata
• Abstract summary: We propose to classify spam email in categories to improve the handle of already detected spam emails. For the task of multi-class spam classification, the use of TF-IDF combined with SVM for the best micro F1 score performance, $95.39%$, and (ii) TD-IDF along with NB for the fastest spam classification, analyzing an email in $2.13$ms.
• Score: 1.8065361710947976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spammers take advantage of email popularity to send indiscriminately unsolicited emails. Although researchers and organizations continuously develop anti-spam filters based on binary classification, spammers bypass them through new strategies, like word obfuscation or image-based spam. For the first time in literature, we propose to classify spam email in categories to improve the handle of already detected spam emails, instead of just using a binary model. First, we applied a hierarchical clustering algorithm to create SPEMC-$11$K (SPam EMail Classification), the first multi-class dataset, which contains three types of spam emails: Health and Technology, Personal Scams, and Sexual Content. Then, we used SPEMC-$11$K to evaluate the combination of TF-IDF and BOW encodings with Na\"ive Bayes, Decision Trees and SVM classifiers. Finally, we recommend for the task of multi-class spam classification the use of (i) TF-IDF combined with SVM for the best micro F1 score performance, $95.39\%$, and (ii) TD-IDF along with NB for the fastest spam classification, analyzing an email in $2.13$ms.

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