most citedClassification of Spam Emails through Hierarchical Clustering and Supervised Learning

17 citations · 22 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach

F. Janez-Martino, R. Alaiz-Rodriguez, V. Gonzalez-Castro +2

Spam emails are unsolicited, annoying and sometimes harmful messages which may contain malware, phishing or hoaxes. Unlike most studies that address the design of efficient anti-sp…

cs.CL2020★ 17 cited

Classification of Spam Emails through Hierarchical Clustering and Supervised Learning

Francisco Jáñez-Martino, Eduardo Fidalgo, Santiago González-Martínez +1

Spammers take advantage of email popularity to send indiscriminately unsolicited emails. Although researchers and organizations continuously develop anti-spam filters based on bina…

cs.CV2020

Perceptual Hashing applied to Tor domains recognition

Rubel Biswas, Roberto A. Vasco-Carofilis, Eduardo Fidalgo Fernandez +2

The Tor darknet hosts different types of illegal content, which are monitored by cybersecurity agencies. However, manually classifying Tor content can be slow and error-prone. To s…

cs.CV2020★ 1 cited

Classifying Suspicious Content in Tor Darknet

Eduardo Fidalgo Fernandez, Roberto Andrés Vasco Carofilis, Francisco Jáñez Martino +1

One of the tasks of law enforcement agencies is to find evidence of criminal activity in the Darknet. However, visiting thousands of domains to locate visual information containing…

cs.CV2020★ 2 cited

Evaluating Performance of an Adult Pornography Classifier for Child Sexual Abuse Detection

Mhd Wesam Al-Nabki, Eduardo Fidalgo, Roberto A. Vasco-Carofilis +2

The information technology revolution has facilitated reaching pornographic material for everyone, including minors who are the most vulnerable in case they were abused. Accuracy a…

cs.CL2020★ 2 cited

Improving Named Entity Recognition in Tor Darknet with Local Distance Neighbor Feature

Mhd Wesam Al-Nabki, Francisco Jañez-Martino, Roberto A. Vasco-Carofilis +2

Name entity recognition in noisy user-generated texts is a difficult task usually enhanced by incorporating an external resource of information, such as gazetteers. However, gazett…