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Fraud Analysis in IP and Next-Generation Networks

Table of Contents:
Definition and Overview
1. Introduction to Telecom Fraud
2. IT Security vs. SP Network Security
3. IP and NGN Vulnerabilities
4. Fraud-Assisting Business Drives
5. IP and NGN Challenges
6. Data Collection
7. Analysis Algorithms
Self-Test
Glossary
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Self-Test
1. Losses due to telecom fraud worldwide are estimated around ______________________.
a. $10 million annually
b. $100 million annually
c. $1 billion annually
d. $40 billion annually
2. Firewalls analyze traffic _______________________.
a. at the IP layer
b. at the application layer
c. at the physical layer
d. at all layers
3. IP networks are less susceptible to fraud than telephony networks since they incorporate various encryption mechanisms.
a. true
b. false
4. Shared mediums allow for easier password sniffing.
a. true
b. false
5. Privacy laws and data protection legislation ______________________________________.
a. allow service providers to easily prosecute fraudsters
b. are handling only IT security issues and not network security issues
c. may sometimes allow a fraudster to continue their scams with a different provider
d. have nothing to do with telecom fraud
6. User location and MAC address information are typically available from ________________.
a. the billing records
b. access network elements
c. core routers and switches
d. RADIUS and other authentication servers
7. Which of the following is an advantage of threshold-based analysis?
a. It doesn't require any user configuration.
b. It practically detects many of the fraud organizations.
c. It is especially useful for detecting fake cellular handles.
8. Which of the following is an advantage of profile-based analysis?
a. It doesn't require any user configuration.
b. It is the simplest method to implement in an FMS.
c. It is very useful for monitoring new subscribers.
9. Expert systems scale better than profile-based systems.
a. true
b. false
10. Which of the following algorithms has the quality that it requires minimal configuration?
a. threshold-based analysis
b. inference rules analysis
c. neural networks

Glossary >>

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