Detalhes
Nome
Pedro Manuel RibeiroCargo
Investigador SéniorDesde
03 maio 2010
Nacionalidade
PortugalCentro
Centro de Sistemas de Computação AvançadaContactos
+351220402963
pedro.p.ribeiro@inesctec.pt
2023
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
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
Autores
Oliveira, HS; Ribeiro, PP; Oliveira, HP;
Publicação
Pattern Recognition and Image Analysis - 11th Iberian Conference, IbPRIA 2023, Alicante, Spain, June 27-30, 2023, Proceedings
Abstract
2023
Autores
Silva, VF; Silva, ME; Ribeiro, P; Silva, FMA;
Publicação
CoRR
Abstract
2023
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
Autor
Maria Hermínia Esteves de Carvalho
Instituição
UP-FCUP
2022
Autor
William Tostes Lobo
Instituição
UP-FEUP
2022
Autor
Hugo Manuel Soares Oliveira
Instituição
UP-FCUP
2022
Autor
Jongmin Han
Instituição
UP-FEUP
2022
Autor
Gonçalo José Marques Ribeiro
Instituição
UP-FEUP
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