research-article

Generating Personalized Algorithms to Learn Bayesian Network Classifiers for Fraud Detection in Web Transactions

Authors:

Gisele L. Pappa,

Adriano C.M. PereiraAuthors Info & Claims

WebMedia '14: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web

Pages 179 - 186

https://doi.org/10.1145/2664551.2664568

Published: 18 November 2014 Publication History

Abstract

The volume of electronic transactions has raised a lot in last years, mainly due to the popularization of e-commerce. We also observe a significant increase in the number of fraud cases, resulting in billions of dollars losses each year worldwide. Therefore, it is essential to develop and apply techniques that can assist in fraud detection. In this direction, we propose an evolutionary algorithm to automatically build Bayesian Network Classifiers (BNCs) tailored to solve the problem of detecting fraudulent transactions. BNCs are powerful classification models that can deal well with data features, missing data and uncertainty. In order to evaluate the techniques, we adopt an economic efficiency metric and apply them to our real dataset. Our results show good performance in fraud detection, presenting gains up to 17%, compared to the actual scenario of the company.

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Cited By

Silva AGiuntini FRanieri CMeneguette RGarcia RRamachandran GKrishnamachari BUeyama J(2023)MADCS: A Middleware for Anomaly Detection and Content Sharing for Blockchain-Based SystemsJournal of Network and Systems Management10.1007/s10922-023-09736-131:3Online publication date: 27-Apr-2023
https://doi.org/10.1007/s10922-023-09736-1

Index Terms

Generating Personalized Algorithms to Learn Bayesian Network Classifiers for Fraud Detection in Web Transactions
1. Computing methodologies
  1. Machine learning
    1. Learning settings
    2. Machine learning approaches
      1. Logical and relational learning
        Inductive logic learning

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Published In

cover image ACM Other conferences

WebMedia '14: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web

November 2014

256 pages

ISBN:9781450332309

DOI:10.1145/2664551

General Chair:
Raoni Kulesza
UFPB - Universidade Federal da Paraíba
,
Program Chair:
Tatiana Tavares
UFPB - Universidade Federal da Paraíba

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SBC: Brazilian Computer Society

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Published: 18 November 2014

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WebMedia'14

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SBC

WebMedia'14: 20th Brazilian Symposium on Multimedia and the Web

November 18 - 21, 2014

João Pessoa, Brazil

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WebMedia '14 Paper Acceptance Rate 25 of 86 submissions, 29%;

Overall Acceptance Rate 270 of 873 submissions, 31%

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Cited By

Silva AGiuntini FRanieri CMeneguette RGarcia RRamachandran GKrishnamachari BUeyama J(2023)MADCS: A Middleware for Anomaly Detection and Content Sharing for Blockchain-Based SystemsJournal of Network and Systems Management10.1007/s10922-023-09736-131:3Online publication date: 27-Apr-2023
https://doi.org/10.1007/s10922-023-09736-1

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