activity
20192024
most citedCascading Machine Learning to Attack Bitcoin Anonymity

39 citations · 42 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CR2024

Unveiling Dynamics and Patterns: A Comprehensive Analysis of Spreading Patterns and Similarities in Low-Labelled Ransomware Families

Francesco Zola, Mikel Gorricho, Jon Ander Medina +2

Ransomware has become one of the most widespread threats, primarily due to its easy deployment and the accessibility to services that enable attackers to raise and obfuscate funds.…

cs.LG20241 cited

Enhancing Law Enforcement Training: A Gamified Approach to Detecting Terrorism Financing

Francesco Zola, Lander Segurola, Erin King +2

Tools for fighting cyber-criminal activities using new technologies are promoted and deployed every day. However, too often, they are unnecessarily complex and hard to use, requiri…

cs.LG2024

NeuralSentinel: Safeguarding Neural Network Reliability and Trustworthiness

Xabier Echeberria-Barrio, Mikel Gorricho, Selene Valencia +1

The usage of Artificial Intelligence (AI) systems has increased exponentially, thanks to their ability to reduce the amount of data to be analyzed, the user efforts and preserving…

cs.LG2021

NBcoded: network attack classifiers based on Encoder and Naive Bayes model for resource limited devices

Lander Segurola-Gil, Francesco Zola, Xabier Echeberria-Barrio +1

In the recent years, cybersecurity has gained high relevance, converting the detection of attacks or intrusions into a key task. In fact, a small breach in a system, application, o…

cs.LG2021

Temporal graph-based approach for behavioural entity classification

Francesco Zola, Lander Segurola, Jan Lukas Bruse +1

Graph-based analyses have gained a lot of relevance in the past years due to their high potential in describing complex systems by detailing the actors involved, their relations an…

cs.LG2020

Generative Adversarial Networks for Bitcoin Data Augmentation

Francesco Zola, Jan Lukas Bruse, Xabier Etxeberria Barrio +2

In Bitcoin entity classification, results are strongly conditioned by the ground-truth dataset, especially when applying supervised machine learning approaches. However, these grou…