Authors:
Roberto Saia
and
Salvatore Carta
Affiliation:
Università di Cagliari, Italy
Keyword(s):
Business Intelligence, Fraud Detection, Pattern Mining, Fourier, Metrics.
Related
Ontology
Subjects/Areas/Topics:
Data and Application Security and Privacy
;
Data Protection
;
Information and Systems Security
;
Intrusion Detection & Prevention
;
Personal Data Protection for Information Systems
;
Security and Privacy for Big Data
;
Security in Information Systems
;
Security Metrics and Measurement
Abstract:
The massive increase in financial transactions made in the e-commerce field has led to an equally massive
increase in the risks related to fraudulent activities. It is a problem directly correlated with the use of credit
cards, considering that almost all the operators that offer goods or services in the e-commerce space allow
their customers to use them for making payments. The main disadvantage of these powerful methods of
payment concerns the fact that they can be used not only by the legitimate users (cardholders) but also by
fraudsters. Literature reports a considerable number of techniques designed to face this problem, although
their effectiveness is jeopardized by a series of common problems, such as the imbalanced distribution and the
heterogeneity of the involved data. The approach presented in this paper takes advantage of a novel evaluation
criterion based on the analysis, in the frequency domain, of the spectral pattern of the data. Such strategy
allows us to ob
tain a more stable model for representing information, with respect to the canonical ones,
reducing both the problems of imbalance and heterogeneity of data. Experiments show that the performance
of the proposed approach is comparable to that of its state-of-the-art competitor, although the model definition
does not use any fraudulent previous case, adopting a proactive strategy able to contrast the cold-start issue.
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