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.