Data Mining and Knowledge Discovery
Histogram-Based Approximation of Set-Valued Query-Answers
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast Incremental Maintenance of Approximate Histograms
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Employing Latent Dirichlet Allocation for fraud detection in telecommunications
Pattern Recognition Letters
An Economic Model of Click Fraud in Publisher Networks
International Journal of Electronic Commerce
Real-time visualization of network behaviors for situational awareness
Proceedings of the Seventh International Symposium on Visualization for Cyber Security
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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Finding telecommunications fraud in masses of call records is more difficult than finding a needle in a haystack. In the haystack problem, there is only one needle that does not look like hay, the pieces of hay all look similar, and neither the needle nor the hay changes much over time. Fraudulent calls may be rare like needles in haystacks, but they are much more challenging to find. Callers are dissimilar, so calls that look like fraud for one account look like expected behavior for another, while all needles look the same. Moreover, fraud has to be found repeatedly, as fast as fraud calls are placed, the nature of fraud changes over time, the extent of fraud is unknown in advance, and fraud may be spread over more than one type of service. For example, calls placed on a stolen wireless telephone may be charged to a stolen credit card. Finding fraud is like finding a needle in a haystack only in the sense of sifting through masses of data to find something rare. This chapter describes some issues involved in creating tools for building fraud systems that are accurate, able to adapt to changing legitimate and fraudulent behavior, and easy to use.