Publications

@article{fidalgo_classifying_2020,

title = {Classifying Suspicious Content in Tor Darknet},

author = {Eduardo Fidalgo and Andrés Carofilis-Vasco and Francisco Jáñez-Martino and Pablo Blanco-Medina},

url = {https://arxiv.org/abs/2005.10086},

year  = {2020},

date = {2020-01-01},

journal = {arXiv e-prints},

pages = {arXiv–2005},

abstract = {This paper proposes Semantic Attention Keypoint Filtering (SAKF) to classify Tor Darknet images by focusing on significant features related to criminal activities. By combining saliency maps with Bag of Visual Words (BoVW), SAKF outperforms CNN approaches (MobileNet v1, ResNet50) and BoVW with dense SIFT descriptors, achieving 87.98% accuracy.},

keywords = {Computer vision, Criminal Activity Detection, Darknet Analysis, Image classification},

pubstate = {published},

tppubtype = {article}

}

@article{fidalgo_classifying_2020-1,

title = {Classifying suspicious content in Tor Darknet},

author = {Eduardo Fidalgo and Andrés Carofilis-Vasco and Francisco Jáñez-Martino and Pablo Blanco-Medina},

url = {https://ui.adsabs.harvard.edu/abs/2020arXiv200510086F/abstract},

year  = {2020},

date = {2020-01-01},

journal = {arXiv preprint arXiv:2005.10086},

abstract = {This paper proposes Semantic Attention Keypoint Filtering (SAKF) to classify Tor Darknet images by focusing on significant features related to criminal activities. By combining saliency maps with Bag of Visual Words (BoVW), SAKF outperforms CNN approaches (MobileNet v1, ResNet50) and BoVW with dense SIFT descriptors, achieving 87.98% accuracy.},

keywords = {Computer vision, Criminal Activity Detection, Darknet Analysis, Image classification},

pubstate = {published},

tppubtype = {article}

}

@article{al_nabki_classifying_2017,

title = {Classifying illegal activities on tor network based on web textual contents},

author = {Wesam Al-Nabki and Eduardo Fidalgo and Enrique Alegre and Iván De Paz-Centeno},

url = {https://aclanthology.org/E17-1004/},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},

pages = {35–43},

abstract = {This paper introduces DUTA, a publicly available dataset of Darknet domains labeled into 26 classes. Using DUTA, a classification study was conducted with TF-IDF and supervised classifiers. Logistic Regression with TF-IDF achieved 96.6% accuracy and a 93.7% F1-score in detecting illegal activities, aiding potential law enforcement tools.},

keywords = {Cybersecurity, Darknet Analysis, Logistic Regression, machine learning, Text classification, TF-IDF},

pubstate = {published},

tppubtype = {article}

}