C4.5: programs for machine learning
C4.5: programs for machine learning
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
Data Mining and Knowledge Discovery
Signature-Based Methods for Data Streams
Data Mining and Knowledge Discovery
Machine Learning
Neural Fraud Detection in Mobile Phone Operations
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
Unsupervised Profiling for Identifying Superimposed Fraud
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A network intrusion detection system based on the artificial neural networks
InfoSecu '04 Proceedings of the 3rd international conference on Information security
A Survey of Voice over IP Security Research
ICISS '09 Proceedings of the 5th International Conference on Information Systems Security
Training a neural-network based intrusion detector to recognize novel attacks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The migration from circuit-switched networks to packet-switched networks necessitates the investigation of related issues such as service delivery, QoS, security, and service fraud and misuse. The latter can be seen as a combination of accounting and security aspects. In traditional telecommunication networks, fraud accounts for annual losses at an average of 3%-5% of the operators' revenue and still increasing at a rate of more than 10% yearly. It is also expected that in VoIP networks, the situation will be worse due to the lack of strong built-in security mechanisms, and the use of open standards. This paper discusses the fraud problem in VoIP networks and evaluates the related available solutions.