Publications
2023
Martínez-Mendoza, Alicia; Sánchez-Paniagua, Manuel; Carofilis-Vasco, Andrés; Jáñez-Martino, Francisco; Fidalgo, Eduardo; Alegre, Enrique
Applying Machine Learning to login URLs for phishing detection Artículo de revista
En: Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023, pp. 487–488, 2023, (Publisher: Universidade de Vigo).
Resumen | Enlaces | BibTeX | Etiquetas: AI, Cybersecurity, machine learning, phishing detection, URL analysis
@article{martinez-mendoza_applying_2023,
title = {Applying Machine Learning to login URLs for phishing detection},
author = {Alicia Martínez-Mendoza and Manuel Sánchez-Paniagua and Andrés Carofilis-Vasco and Francisco Jáñez-Martino and Eduardo Fidalgo and Enrique Alegre},
url = {https://dialnet.unirioja.es/servlet/articulo?codigo=9044941},
year = {2023},
date = {2023-01-01},
journal = {Actas de las VIII Jornadas Nacionales de Investigación en Ciberseguridad: Vigo, 21 a 23 de junio de 2023},
pages = {487–488},
abstract = {This paper explores the application of machine learning for phishing detection using login URLs. By analyzing URL patterns and features, the study aims to differentiate between legitimate and phishing websites. Various machine learning models are evaluated to enhance detection accuracy, providing a proactive approach to cybersecurity threats.},
note = {Publisher: Universidade de Vigo},
keywords = {AI, Cybersecurity, machine learning, phishing detection, URL analysis},
pubstate = {published},
tppubtype = {article}
}
2019
Domínguez, Víctor; Fidalgo, Eduardo; Biswas, Rubel; Alegre, Enrique; Fernández-Robles, Laura
Application of extractive text summarization algorithms to speech-to-text media Artículo de revista
En: Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14, pp. 540–550, 2019, (Publisher: Springer International Publishing).
Resumen | Enlaces | BibTeX | Etiquetas: AI, machine learning, natural languaje processing, speech to text, Text summarization
@article{dominguez_application_2019,
title = {Application of extractive text summarization algorithms to speech-to-text media},
author = {Víctor Domínguez and Eduardo Fidalgo and Rubel Biswas and Enrique Alegre and Laura Fernández-Robles},
url = {https://link.springer.com/chapter/10.1007/978-3-030-29859-3_46},
year = {2019},
date = {2019-01-01},
journal = {Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14},
pages = {540–550},
abstract = {This paper evaluates six extractive text summarization algorithms for speech-to-text summarization. The study assesses Luhn, TextRank, LexRank, LSA, SumBasic, and KLSum using ROUGE metrics on two datasets (DUC2001 and OWIDSum). Additionally, five speech documents from the ISCI Corpus were transcribed using Google Cloud Speech API and summarized. Results indicate that Luhn and TextRank perform best for extractive speech-to-text summarization.},
note = {Publisher: Springer International Publishing},
keywords = {AI, machine learning, natural languaje processing, speech to text, Text summarization},
pubstate = {published},
tppubtype = {article}
}