Discovery of fraud rules for telecommunications—challenges and solutions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 1998 conference on Advances in neural information processing systems II
Anomaly-based intrusion detection: privacy concerns and other problems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on recent advances in intrusion detection systems
Case Studies for Method and Tool Evaluation
IEEE Software
A Synthetic Fraud Data Generation Methodology
ICICS '02 Proceedings of the 4th International Conference on Information and Communications Security
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A distributed database architecture for global roaming in next-generation mobile networks
IEEE/ACM Transactions on Networking (TON)
How to Build a Business Rules Engine: Extending Application Functionality through Metadata Engineering (The Morgan Kaufmann Series in Data Management Systems)
New Regulations to the Next Generation Network
CMC '09 Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 02
A service-centric model for intrusion detection in next-generation networks
Computer Standards & Interfaces
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The deployment of Next-Generation Networks (NGN) is a challenge that requires integrating heterogeneous services into a global system of All-IP telecommunications. These networks carry voice, data, and multimedia traffic over the Internet, providing users with the information they want in any format, amount, device, place or moment. Still, there are certain issues, such as the emerging security risks or the billing paradigms of the services offered, which demand deeper research in order to guarantee the stability and the revenue of such systems. Against this background, we analyse the security requirements of NGN and introduce a fraud management system based on misuse detection for Voice over IP services. Specifically, we address a fraud detection framework consisting of a rule engine built over a knowledge base. We detail the architecture of our model and describe a case study illustrating a possible fraud and how our system detects it, proving in this way, its feasibility in this task.