70 citations · 89 across the 12 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…
Finding NeMo: Fishing in banking networks using network motifs
Xavier Fontes, David Aparício, Maria Inês Silva +3
Banking fraud causes billion-dollar losses for banks worldwide. In fraud detection, graphs help understand complex transaction patterns and discovering new fraud schemes. This work…
Active learning for imbalanced data under cold start
Ricardo Barata, Miguel Leite, Ricardo Pacheco +3
Modern systems that rely on Machine Learning (ML) for predictive modelling, may suffer from the cold-start problem: supervised models work well but, initially, there are no labels,…
GuiltyWalker: Distance to illicit nodes in the Bitcoin network
Catarina Oliveira, João Torres, Maria Inês Silva +3
Money laundering is a global phenomenon with wide-reaching social and economic consequences. Cryptocurrencies are particularly susceptible due to the lack of control by authorities…
Machine learning methods to detect money laundering in the Bitcoin blockchain in the presence of label scarcity
Joana Lorenz, Maria Inês Silva, David Aparício +2
Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless people and economies. Cry…
ARMS: Automated rules management system for fraud detection
David Aparício, Ricardo Barata, João Bravo +2
Fraud detection is essential in financial services, with the potential of greatly reducing criminal activities and saving considerable resources for businesses and customers. We ad…
Interleaved Sequence RNNs for Fraud Detection
Bernardo Branco, Pedro Abreu, Ana Sofia Gomes +3
Payment card fraud causes multibillion dollar losses for banks and merchants worldwide, often fueling complex criminal activities. To address this, many real-time fraud detection s…