MOBILE FRAUD DETECTION

MOBILE FRAUD DETECTION - MOBILE FRAUD DETECTOR MOBILE FRAUD...

Info iconThis preview shows pages 1–6. Sign up to view the full content.

View Full Document Right Arrow Icon
MOBILE FRAUD DETECTOR MOBILE FRAUD DETECTION By, Mr. Suhas P.Karve , VIII Sem Mr. Vinayak S.Nadkarni , VIII Sem Hubli-580031 KARNATAKA 1
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
MOBILE FRAUD DETECTOR Table of contents 1. Abstract 2. Introduction 3. Possible frauds and their indicators 4. User profiling 5. Rule-based approach to fraud detection 6. Neural network based approach to fraud detection 7. Conclusion 2
Background image of page 2
MOBILE FRAUD DETECTOR Chapter 1 Abstract This paper discusses the status of research on detection of fraud undertaken in mobile system. A first task has been the identification of possible fraud scenarios and of typical fraud indicators, which can be mapped to data in toll tickets. Currently, this project is exploring the detection of fraudulent behaviour based on a combination of absolute and differential usage. Three approaches are being investigated: a rule-based approach, and two approaches based on neural networks, where both supervised and unsupervised learning are considered. Special attention is being paid to the feasibility of the implementations. 3
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
MOBILE FRAUD DETECTOR Chapter 2 Introduction It is estimated that the mobile communications industry loses several million customers per year due to fraud. Therefore, prevention and early detection of fraudulent activity is an important goal for network operators. It is clear that the additional security measures taken in GSM and in the future UMTS (Universal Mobile Telecommunications System) make these networks less vulnerable to fraud than the analogue networks. Nevertheless, certain types of commercial fraud are very hard to preclude by technical means. It is also anticipated that the introduction of new services can lead to the development of new ways to defraud the system. The use of sophisticated fraud detection techniques can assist in early detection of commercial frauds, and will also reduce the effectivity of technical frauds. The remainder of this paper is organized as follows: Chapter 1 discusses the identification of possible fraud scenarios and of fraud indicators Chapter 2 discusses the general approach of user profiling, Chapter 3 presents the rule-based approach Chapter 4 presents the neural net-based approach to fraud detection. 4
Background image of page 4
MOBILE FRAUD DETECTOR Chapter 3 Possible frauds and their indicators 3.1 Possible Frauds: Two way Classification of Frauds: The first stage of the work consists of the identification of possible fraud scenarios in telecommunications networks and particularly in mobile phone networks. These scenarios have been classified by the technical manner in which they are
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 6
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 03/26/2011 for the course IT 101 taught by Professor Dontknow during the Spring '07 term at Northern Virginia.

Page1 / 16

MOBILE FRAUD DETECTION - MOBILE FRAUD DETECTOR MOBILE FRAUD...

This preview shows document pages 1 - 6. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online