Interconnect bypass fraud detection: a case study

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Veloso, B (Autor) Outra; Tabassum, S (Autor) Outra; Martins, C (Autor) Outra; Espanha, R (Autor) Outra; Azevedo, R (Autor) Outra; João Gama (Autor) FEP

Revista

Título: Annales des Telecommunications/Annals of Telecommunications

Vol. 75 Nº 9-10

Páginas: 583-596

ISSN: 0003-4347

Indexação

ISI Web of Knowledge - 3 Citações

Scopus - 5 Citações

Outras Informações

ID Authenticus: P-00S-R8R

DOI: 10.1007/s12243-020-00808-w

Abstract (EN): The high asymmetry of international termination rates is fertile ground for the appearance of fraud in telecom companies. International calls have higher values when compared with national ones, which raises the attention of fraudsters. In this paper, we present a solution for a real problem called interconnect bypass fraud, more specifically, a newly identified distributed pattern that crosses different countries and keeps fraudsters from being tracked by almost all fraud detection techniques. This problem is one of the most expressive in the telecommunication domain, and it has some abnormal behaviours like the occurrence of a burst of calls from specific numbers. Based on this assumption, we propose the adoption of a new fast forgetting technique that works together with the Lossy Counting algorithm. We apply frequent set mining to capture distributed patterns from different countries. Our goal is to detect as soon as possible items with abnormal behaviours, e.g., bursts of calls, repetitions, mirrors, distributed behaviours and a small number of calls spread by a vast set of destination numbers. The results show that the application of different techniques improves the detection ratio and not only complements the techniques used by the telecom company but also improves the performance of the Lossy Counting algorithm in terms of run-time, memory used and sensibility to detect the abnormal behaviours. Additionally, the application of frequent set mining allows us to capture distributed fraud patterns.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14