User profiling in personalization applications through rule discovery and validation
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
KDEX '97 Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop
Testing the fraud detection ability of different user profiles by means of FF-NN classifiers
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Integrated expert system applied to the analysis of non-technical losses in power utilities
Expert Systems with Applications: An International Journal
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
Learning curve in concept drift while using active learning paradigm
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Expert Systems with Applications: An International Journal
A probabilistic approach to fraud detection in telecommunications
Knowledge-Based Systems
Preprocessing unbalanced data using support vector machine
Decision Support Systems
Probabilistic distance based abnormal pattern detection in uncertain series data
Knowledge-Based Systems
Early security classification of skype users via machine learning
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
Hi-index | 12.05 |
Telecommunications fraud not only burdens telecom provider's accountings but burdens individual users as well. The latter are particularly affected in the case of superimposed fraud where the fraudster uses a legitimate user's account in parallel with the user. These cases are usually identified after user complaints for excess billing. However, inside the network of a large firm or organization, superimposed fraud may go undetected for some time. The present paper deals with the detection of fraudulent telecom activity inside large organizations' premises. Focus is given on superimposed fraud detection. The problem is attacked via the construction of an expert system which incorporates both the network administrator's expert knowledge and knowledge derived from the application of data mining techniques on real world data.