Scam and fraud detection in VoIP Networks: Analysis and countermeasures using user profiling

Author

Kapourniotis, Theodoros ; Dagiuklas, Tasos ; Polyzos, George ; Alefragkis, Panagiotis

Author_Institution

Dept. of Telecommun. Syst. & Networks, TEI of Mesolonghi Nafpaktos, Mesolonghi Nafpaktos, Greece

fYear

2011

fDate

Aug. 31 2011-Sept. 3 2011

Firstpage

1

Lastpage

5

Abstract

This paper presents a VoIP Fraud Detection Framework by exploiting VoIP and/or network-OSS/BSS vulnerabilities. This can be accomplished by analyzing the behavior of the VoIP user using an ontology model so that different types of fraud scenarios could be identified. Using this ontology, an unsupervised learning algorithm has been implemented that describes the user behavior and/or the correlation among various features by analyzing CDR data. The statistical model that has been used is a Bayesian Network. The performance of the proposed model is optimized (minimizing the percentage of false alarms) by configuring the parameters of the Bayesian Network properly.

Keywords

Bayes methods; Internet telephony; behavioural sciences computing; data analysis; fraud; minimisation; ontologies (artificial intelligence); unsupervised learning; Bayesian network; CDR data; VoIP fraud detection framework; VoIP user behavior; false alarm percentage minimization; network OSS-BSS vulnerabilities; ontology model; scam detection; statistical model; unsupervised learning algorithm; user profiling; Bayesian methods; Computational modeling; Correlation; Data mining; Security; Training; Unified modeling language; VoIP Fraud Detection; VoIP Security;

fLanguage

English

Publisher

ieee

Conference_Titel

FITCE Congress (FITCE), 2011 50th

Conference_Location

Palermo

Print_ISBN

978-1-4577-1208-1

Type

conf

DOI

10.1109/FITCE.2011.6133427

Filename

6133427