Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
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
Proceedings of the 1998 conference on Advances in neural information processing systems II
Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
How to Increase Security in Mobile Networks by Anomaly Detection
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Network intrusion and fault detection: a statistical anomaly approach
IEEE Communications Magazine
An Application of Decision Trees for Rule Extraction Towards Telecommunications Fraud Detection
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Designing an expert system for fraud detection in private telecommunications networks
Expert Systems with Applications: An International Journal
Web user behavioral profiling for user identification
Decision Support Systems
Expert Systems with Applications: An International Journal
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Telecommunications fraud has drawn the attention in research due to the huge economic burden on companies and to the interesting aspect of users' behavior characterization. In the present paper, we deal with the issue of user characterization. Several real cases of defrauded user accounts for different user profiles were studied. Each profile's ability to characterize user behavior in order to discriminate normal activity from fraudulent one was tested. Feed-forward neural networks were used as classifiers. It is found that summary characteristics of user's behavior perform better than detailed ones towards this task.