Publications

@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}

}

@article{al-nabki_author_2023,

title = {Author Correction: Short text classification approach to identify child sexual exploitation material},

author = {Wesam Al-Nabki and Eduardo Fidalgo and Enrique Alegre and Rocío Alaiz-Rodríguez},

url = {https://www.nature.com/articles/s41598-023-45265-2},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Scientific Reports},

volume = {13},

number = {1},

pages = {17840},

abstract = {This paper presents a method to identify Child Sexual Exploitation Material (CSEM) files by analyzing file names and paths instead of content, aiding law enforcement in time-sensitive investigations. The approach tackles obfuscation using character n-grams, binary, and orthographic features. Two classification strategies are proposed: one combining separate file name and path classifiers, and another iterating over the path. Six machine learning and deep learning models were tested, with the best achieving an F1 score of 0.988, making it a promising tool for law enforcement agencies.},

note = {Publisher: Nature Publishing Group UK London},

keywords = {CSEM detection, law enforcement, machine learning, test classification},

pubstate = {published},

tppubtype = {article}

}