Publications
2023
1.
Al-Nabki, Wesam; 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 = {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},
urldate = {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.
Alegre, Enrique
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{alegre_supervised_2019,
title = {SUPERVISED MACHINE LEARNING FOR CLASSIFICATION, MINING, AND RANKING OF ILLEGAL WEB CONTENTS},
author = {Enrique Alegre},
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.
2018
3.
Joshi, Akanksha; Fidalgo, Eduardo; Alegre, Enrique; Al-Nabki, Wesam
Extractive Text Summarization in Dark Web: A Preliminary Study Artículo de revista
En: International Conference of Applications of Intelligent Systems, 2018.
Resumen | Enlaces | BibTeX | Etiquetas: Dark Web, Text classification, TOR Network
@article{joshi_extractive_2018,
title = {Extractive Text Summarization in Dark Web: A Preliminary Study},
author = {Akanksha Joshi and Eduardo Fidalgo and Enrique Alegre and Wesam Al-Nabki},
url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=yATJZvcAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=yATJZvcAAAAJ:iH-uZ7U-co4C},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {International Conference of Applications of Intelligent Systems},
abstract = {This paper explores automatic text summarization applied to illegal content extracted from onion websites on the Tor network. The goals include evaluating the feasibility of summarizing such content, comparing summarization methods, and introducing a new dataset called "OWIDSumm," which contains manually curated summaries for 60 documents related to illicit services.},
keywords = {Dark Web, Text classification, TOR Network},
pubstate = {published},
tppubtype = {article}
}
This paper explores automatic text summarization applied to illegal content extracted from onion websites on the Tor network. The goals include evaluating the feasibility of summarizing such content, comparing summarization methods, and introducing a new dataset called "OWIDSumm," which contains manually curated summaries for 60 documents related to illicit services.