1 citations · 2 across the 8 of their papers we have counts for
12 papers
Adversarial training for tabular data with attack propagation
Tiago Leon Melo, João Bravo, Marco O. P. Sampaio +4
Adversarial attacks are a major concern in security-centered applications, where malicious actors continuously try to mislead Machine Learning (ML) models into wrongly classifying…
The GANfather: Controllable generation of malicious activity to improve defence systems
Ricardo Ribeiro Pereira, Jacopo Bono, João Tiago Ascensão +3
Machine learning methods to aid defence systems in detecting malicious activity typically rely on labelled data. In some domains, such labelled data is unavailable or incomplete. I…
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +4
Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics…
Lightweight Automated Feature Monitoring for Data Streams
João Conde, Ricardo Moreira, João Torres +5
Monitoring the behavior of automated real-time stream processing systems has become one of the most relevant problems in real world applications. Such systems have grown in complex…
Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício +4
Money laundering is a global problem that concerns legitimizing proceeds from serious felonies (1.7-4 trillion euros annually) such as drug dealing, human trafficking, or corruptio…
GUDIE: a flexible, user-defined method to extract subgraphs of interest from large graphs
Maria Inês Silva, David Aparício, Beatriz Malveiro +2
Large, dense, small-world networks often emerge from social phenomena, including financial networks, social media, or epidemiology. As networks grow in importance, it is often nece…