Publications
2023
1.
Nabki, MHD Wesam Al; Fidalgo, Eduardo; Alegre, Enrique; Chaves, Deisy
Supervised ranking approach to identify infLuential websites in the darknet Artículo de revista
En: Applied Intelligence, vol. 53, no 19, pp. 22952–22968, 2023, (Publisher: Springer US New York).
Resumen | Enlaces | BibTeX | Etiquetas: Criminal Detection, Domain Ranking, law enforcement, Learning-to Rank, TOR Network
@article{al_nabki_supervised_2023,
title = {Supervised ranking approach to identify infLuential websites in the darknet},
author = {MHD Wesam Al Nabki and Eduardo Fidalgo and Enrique Alegre and Deisy Chaves},
url = {https://link.springer.com/article/10.1007/s10489-023-04671-9},
year = {2023},
date = {2023-01-01},
journal = {Applied Intelligence},
volume = {53},
number = {19},
pages = {22952–22968},
abstract = {This paper introduces a supervised ranking framework to identify the most influential domains in the Tor network, focusing on criminal activities. It uses 40 features from various sources to train a learning-to-rank model, achieving an NDCG of 0.93 for top-10 drug-related domains. The framework outperforms link-based methods and demonstrates that user-visible text is key for effective ranking, aiding law enforcement in detecting suspicious Tor domains.},
note = {Publisher: Springer US New York},
keywords = {Criminal Detection, Domain Ranking, law enforcement, Learning-to Rank, TOR Network},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a supervised ranking framework to identify the most influential domains in the Tor network, focusing on criminal activities. It uses 40 features from various sources to train a learning-to-rank model, achieving an NDCG of 0.93 for top-10 drug-related domains. The framework outperforms link-based methods and demonstrates that user-visible text is key for effective ranking, aiding law enforcement in detecting suspicious Tor domains.
2019
2.
GUTIÉRREZ, DR ENRIQUE ALEGRE
SUPERVISED MACHINE LEARNING FOR CLASSIFICATION, MINING, AND RANKING OF ILLEGAL WEB CONTENTS Tesis doctoral
UNIVERSITY OF LEÓN, 2019.
Resumen | Enlaces | BibTeX | Etiquetas: Darknet, Illegal Activities, Pastebin, Text classification, TOR Network
@phdthesis{gutierrez_supervised_2019,
title = {SUPERVISED MACHINE LEARNING FOR CLASSIFICATION, MINING, AND RANKING OF ILLEGAL WEB CONTENTS},
author = {DR ENRIQUE ALEGRE GUTIÉRREZ},
url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=yATJZvcAAAAJ&cstart=100&pagesize=100&sortby=title&citation_for_view=yATJZvcAAAAJ:ldfaerwXgEUC},
year = {2019},
date = {2019-01-01},
school = {UNIVERSITY OF LEÓN},
abstract = {This thesis introduces algorithms, methods, and datasets aimed at classifying, mining information, and ranking web domains or similar resources containing text. The focus is on detecting web content that may indicate illegal activities, particularly in the Tor Darknet and Online Notepad Services (ONS), like Pastebin. Motivated by a collaboration with INCIBE, the research addresses the identification of criminal content in these areas, based on the assumption that the Tor network harbors a significant amount of illicit activity.},
keywords = {Darknet, Illegal Activities, Pastebin, Text classification, TOR Network},
pubstate = {published},
tppubtype = {phdthesis}
}
This thesis introduces algorithms, methods, and datasets aimed at classifying, mining information, and ranking web domains or similar resources containing text. The focus is on detecting web content that may indicate illegal activities, particularly in the Tor Darknet and Online Notepad Services (ONS), like Pastebin. Motivated by a collaboration with INCIBE, the research addresses the identification of criminal content in these areas, based on the assumption that the Tor network harbors a significant amount of illicit activity.