Hancock: a language for extracting signatures from data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Signature-Based Methods for Data Streams
Data Mining and Knowledge Discovery
Computer and Intrusion Forensics
Computer and Intrusion Forensics
Establishing fraud detection patterns based on signatures
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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Fraud in telecommunications is increasing dramatically with the expansion of modern technology, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud,. Fraudsters are adaptive, searching systematically for new ways to commit fraud and, in most of the cases, will usually find some way to circumvent companies prevention measures. In this paper we expose some of the ways in which fraud is being used against organizations, evaluating the limitations of existing strategies and methods to detect and prevent it in todays telecommunications companies. Additionally, we expose a data mining profiling technique based on signatures that was developed for a real mobile telecommunications network operator and integrated into one of its Fraud Management Systems (FMS), currently under operation.