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de interesse
Detalhes

Detalhes

  • Nome

    Pedro Manuel Ribeiro
  • Cargo

    Investigador Sénior
  • Desde

    03 maio 2010
Publicações

2023

Improving the Characterization and Comparison of Football Players with Spatial Flow Motifs

Autores
Barbosa, A; Ribeiro, P; Dutra, I;

Publicação
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2

Abstract
Association Football is probably the world's most popular sport. Being able to characterise and compare football players is therefore a very important and impactful task. In this work we introduce spatial flow motifs as an extension of previous work on this problem, by incorporating both temporal and spatial information into the network analysis of football data. Our approach considers passing sequences and the role of the player in those sequences, complemented with the physical position of the field where the passes occurred. We provide experimental results of our proposed methodology on real-life event data from the Italian League, showing we can more accurately identify players when compared to using purely topological data.

2023

Towards the Concept of Spatial Network Motifs

Autores
Ferreira, J; Barbosa, A; Ribeiro, P;

Publicação
COMPLEX NETWORKS AND THEIR APPLICATIONS XI, COMPLEX NETWORKS 2022, VOL 2

Abstract
Many complex systems exist in the physical world and therefore can be modeled by networks in which their nodes and edges are embedded in space. However, classical network motifs only use purely topological information and disregard other features. In this paper we introduce a novel and general subgraph abstraction that incorporates spatial information, therefore enriching its characterization power. Moreover, we describe and implement a method to compute and count our spatial subgraphs in any given network. We also provide initial experimental results by using our methodology to produce spatial fingerprints of real road networks, showcasing its discrimination power and how it captures more than just simple topology.

2023

MHVG2MTS: Multilayer Horizontal Visibility Graphs for Multivariate Time Series Analysis

Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, FMA;

Publicação
CoRR

Abstract

2023

The GANfather: Controllable generation of malicious activity to improve defence systems

Autores
Pereira, RR; Bono, J; Ascensao, JT; Aparício, D; Ribeiro, P; Bizarro, P;

Publicação
PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023

Abstract
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. In practice this can lead to low detection rates and high false positive rates, which characterise for example anti-money laundering systems. In fact, it is estimated that 1.7-4 trillion euros are laundered annually and go undetected. We propose The GANfather, a method to generate samples with properties of malicious activity, without label requirements. We propose to reward the generation of malicious samples by introducing an extra objective to the typical Generative Adversarial Networks (GANs) loss. Ultimately, our goal is to enhance the detection of illicit activity using the discriminator network as a novel and robust defence system. Optionally, we may encourage the generator to bypass pre-existing detection systems. This setup then reveals defensive weaknesses for the discriminator to correct. We evaluate our method in two real-world use cases, money laundering and recommendation systems. In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system. In the latter, we recommend the target item to a broad user base with as few as 30 synthetic attackers. In both cases, we train a new defence system to capture the synthetic attacks.

2023

The GANfather: Controllable generation of malicious activity to improve defence systems

Autores
Pereira, RR; Bono, J; Ascensao, JT; Aparício, D; Ribeiro, P; Bizarro, P;

Publicação
PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023

Abstract
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. In practice this can lead to low detection rates and high false positive rates, which characterise for example anti-money laundering systems. In fact, it is estimated that 1.7-4 trillion euros are laundered annually and go undetected. We propose The GANfather, a method to generate samples with properties of malicious activity, without label requirements. We propose to reward the generation of malicious samples by introducing an extra objective to the typical Generative Adversarial Networks (GANs) loss. Ultimately, our goal is to enhance the detection of illicit activity using the discriminator network as a novel and robust defence system. Optionally, we may encourage the generator to bypass pre-existing detection systems. This setup then reveals defensive weaknesses for the discriminator to correct. We evaluate our method in two real-world use cases, money laundering and recommendation systems. In the former, our method moves cumulative amounts close to 350 thousand dollars through a network of accounts without being detected by an existing system. In the latter, we recommend the target item to a broad user base with as few as 30 synthetic attackers. In both cases, we train a new defence system to capture the synthetic attacks.

Teses
supervisionadas

2022

On the Summarization of Complex Networks

Autor
Isac Daniel de Figueiredo Novo

Instituição
UP-FCUP

2022

Building Blocks of Networks

Autor
Luciano Polónia Gonçalves Grácio

Instituição
UP-FCUP

2022

Spotting Fraud: Detecting patterns and red flags in financial networks

Autor
Joana Isabel Cortez Trindade

Instituição
UP-FEP

2022

Multidimensional Time Series Analysis: A Complex Networks Approach

Autor
Vanessa Alexandra Freitas da Silva

Instituição
UP-FCUP

2022

Fraud Detection and Anti-money Laundering using Graph Techniques

Autor
Ahmad Naser Eddin

Instituição
UP-FCUP