Publications

@article{janez-martino_spam_2024-1,

title = {Spam hierarchical clustering for campaigns spotting and topic-based classification [Póster]},

author = {Francisco Jáñez-Martino and Andrés Carofilis-Vasco 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}

}

@article{al_nabki_classifying_2017,

title = {Classifying illegal activities on tor network based on web textual contents},

author = {Wesam Al-Nabki and Eduardo Fidalgo and Enrique Alegre and Iván De Paz-Centeno},

url = {https://aclanthology.org/E17-1004/},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},

pages = {35–43},

abstract = {This paper introduces DUTA, a publicly available dataset of Darknet domains labeled into 26 classes. Using DUTA, a classification study was conducted with TF-IDF and supervised classifiers. Logistic Regression with TF-IDF achieved 96.6% accuracy and a 93.7% F1-score in detecting illegal activities, aiding potential law enforcement tools.},

keywords = {Cybersecurity, Darknet Analysis, Logistic Regression, machine learning, Text classification, TF-IDF},

pubstate = {published},

tppubtype = {article}

}