Publications
2024
1.
Martino, Francisco Jáñez; Carofilis, Andrés; Rodríguez, Rocío Alaiz; Castro, Víctor González; Fidalgo, Eduardo; Alegre, Enrique
Spam hierarchical clustering for campaigns spotting and topic-based classification [Póster] Artículo de revista
En: 2024, (Publisher: Universidad de Sevilla. Escuela Técnica Superior de Ingeniería Informática).
Resumen | Enlaces | BibTeX | Etiquetas: Cybersecurity, Logistic Regression, Multi-classification, Spam detection
@article{janez_martino_spam_2024,
title = {Spam hierarchical clustering for campaigns spotting and topic-based classification [Póster]},
author = {Francisco Jáñez Martino and Andrés Carofilis and Rocío Alaiz Rodríguez and Víctor González Castro and Eduardo Fidalgo and Enrique Alegre},
url = {https://idus.us.es/items/9828eae7-9cec-4574-8863-99e9020e1770},
year = {2024},
date = {2024-01-01},
abstract = {This article develops spam email multiclassification systems for cybersecurity, using two datasets: SPEMC-15K-E (English) and SPEMC-15K-S (Spanish). The datasets are classified into eleven categories. The best results for English (F1-score: 0.953, 94.6% accuracy) were achieved with TF-IDF and Logistic Regression, while for Spanish, TF-IDF and Naïve Bayes achieved an F1-score of 0.945 and 98.5% accuracy. TF-IDF with Logistic Regression also had the fastest processing time (2ms per email for English and 2.2ms for Spanish).},
note = {Publisher: Universidad de Sevilla. Escuela Técnica Superior de Ingeniería Informática},
keywords = {Cybersecurity, Logistic Regression, Multi-classification, Spam detection},
pubstate = {published},
tppubtype = {article}
}
This article develops spam email multiclassification systems for cybersecurity, using two datasets: SPEMC-15K-E (English) and SPEMC-15K-S (Spanish). The datasets are classified into eleven categories. The best results for English (F1-score: 0.953, 94.6% accuracy) were achieved with TF-IDF and Logistic Regression, while for Spanish, TF-IDF and Naïve Bayes achieved an F1-score of 0.945 and 98.5% accuracy. TF-IDF with Logistic Regression also had the fastest processing time (2ms per email for English and 2.2ms for Spanish).
2023
2.
Jáñez-Martino, Francisco; Alaiz-Rodríguez, Rocío; González-Castro, Víctor; Fidalgo, Eduardo; Alegre, Enrique
Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach Artículo de revista
En: Applied Soft Computing, vol. 139, pp. 110226, 2023, ISSN: 1568-4946.
Resumen | Enlaces | BibTeX | Etiquetas: Hidden text, Image-based spam, Multi-classification, Spam detection, Term frequency, Text classification, Word embedding
@article{JANEZMARTINO2023110226b,
title = {Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach},
author = {Francisco Jáñez-Martino and Rocío Alaiz-Rodríguez and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},
url = {https://www.sciencedirect.com/science/article/pii/S1568494623002442},
doi = {https://doi.org/10.1016/j.asoc.2023.110226},
issn = {1568-4946},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Applied Soft Computing},
volume = {139},
pages = {110226},
abstract = {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.},
keywords = {Hidden text, Image-based spam, Multi-classification, Spam detection, Term frequency, Text classification, Word embedding},
pubstate = {published},
tppubtype = {article}
}
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.
3.
Jáñez-Martino, Francisco; Alaiz-Rodríguez, Rocío; González-Castro, Víctor; Fidalgo, Eduardo; Alegre, Enrique
Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach Artículo de revista
En: Applied Soft Computing, vol. 139, pp. 110226, 2023, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: Hidden text, Image-based spam, Multi-classification, Spam detection, Term frequency, Text classification, Word embedding
@article{janez-martino_classifying_2023,
title = {Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach},
author = {Francisco Jáñez-Martino and Rocío Alaiz-Rodríguez and Víctor González-Castro and Eduardo Fidalgo and Enrique Alegre},
url = {https://www.sciencedirect.com/science/article/pii/S1568494623002442},
year = {2023},
date = {2023-01-01},
journal = {Applied Soft Computing},
volume = {139},
pages = {110226},
abstract = {This paper introduces two novel datasets, SPEMC-15K-E and SPEMC-15K-S, containing 15K spam emails each in English and Spanish. The emails are categorized into 11 classes using hierarchical clustering. Evaluation of 16 classification pipelines reveals that TF-IDF with Logistic Regression achieves the highest performance for the English dataset (F1 score of 0.953, accuracy of 94.6%), while TF-IDF with Naïve Bayes performs best for Spanish (F1 score of 0.945, accuracy of 98.5%). TF-IDF with LR is also the fastest for both languages.},
note = {Publisher: Elsevier},
keywords = {Hidden text, Image-based spam, Multi-classification, Spam detection, Term frequency, Text classification, Word embedding},
pubstate = {published},
tppubtype = {article}
}
This paper introduces two novel datasets, SPEMC-15K-E and SPEMC-15K-S, containing 15K spam emails each in English and Spanish. The emails are categorized into 11 classes using hierarchical clustering. Evaluation of 16 classification pipelines reveals that TF-IDF with Logistic Regression achieves the highest performance for the English dataset (F1 score of 0.953, accuracy of 94.6%), while TF-IDF with Naïve Bayes performs best for Spanish (F1 score of 0.945, accuracy of 98.5%). TF-IDF with LR is also the fastest for both languages.