One-Class Naïve Bayesian Classifier for Toll Fraud Detection

Pilsung KANG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.5   pp.1353-1357
Publication Date: 2014/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.1353
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
Keyword: 
one-class Naïve Bayesian classifier,  toll fraud detection,  genetic algorithm,  novelty detection,  

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Summary: 
In this paper, a one-class Naïve Bayesian classifier (One-NB) for detecting toll frauds in a VoIP service is proposed. Since toll frauds occur irregularly and their patterns are too diverse to be generalized as one class, conventional binary-class classification is not effective for toll fraud detection. In addition, conventional novelty detection algorithms have struggled with optimizing their parameters to achieve a stable detection performance. In order to resolve the above limitations, the original Naïve Bayesian classifier is modified to handle the novelty detection problem. In addition, a genetic algorithm (GA) is employed to increase efficiency by selecting significant variables. In order to verify the performance of One-NB, comparative experiments using five well-known novelty detectors and three binary classifiers are conducted over real call data records (CDRs) provided by a Korean VoIP service company. The experimental results show that One-NB detects toll frauds more accurately than other novelty detectors and binary classifiers when the toll frauds rates are relatively low. In addition, The performance of One-NB is found to be more stable than the benchmark methods since no parameter optimization is required for One-NB.