RT info:eu-repo/semantics/article
T1 Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach
A1 Jáñez Martino, Francisco
A1 Alaiz Rodríguez, Rocío
A1 González Castro, Víctor
A1 Fidalgo Fernández, Eduardo
A1 Alegre Gutiérrez, Enrique
A2 Ingenieria de Sistemas y Automatica
K1 Informática
K1 Spam detection
K1 Multi-classification
K1 Image-based spam
K1 Hidden text
K1 Text classification
K1 Word embedding
K1 Term frequency
AB [EN] Spam emails are unsolicited, annoying and sometimes harmful messages which may contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient anti-spam filters, we approach the spam email problem from a different and novel perspective. Focusing on the needs of cybersecurity units, we follow a topic-based approach for addressing the classification of spam email into multiple categories. We propose SPEMC-15K-E and SPEMC-15K-S, two novel datasets with approximately 15K emails each in English and Spanish, respectively, and we label them using agglomerative hierarchical clustering into 11 classes. We evaluate 16 pipelines, combining four text representation techniques -Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words, Word2Vec and BERT- and four classifiers: Support Vector Machine, Näive Bayes, Random Forest and Logistic Regression. Experimental results show that the highest performance is achieved with TF-IDF and LR for the English dataset, with a F1 score of 0.953 and an accuracy of 94.6%, and while for the Spanish dataset, TF-IDF with NB yields a F1 score of 0.945 and 98.5% accuracy. Regarding the processing time, TF-IDF with LR leads to the fastest classification, processing an English and Spanish spam email in 2ms and 2.2ms on average, respectively.
PB Elsevier
SN 1568-4946
LK http://hdl.handle.net/10612/15999
UL http://hdl.handle.net/10612/15999
NO Jáñez-Martino, F., Alaiz-Rodríguez, R., González-Castro, V., Fidalgo, E., & Alegre, E. (2023). Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach. Applied Soft Computing, 139. https://doi.org/10.1016/J.ASOC.2023.110226
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 07-may-2024