IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Communications of the ACM
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining and Knowledge Discovery
Neural Data Mining for Credit Card Fraud Detection
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Editorial: Recent advances in data mining
Engineering Applications of Artificial Intelligence
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A non-technical loss (NTL) is defined as any consumed energy or service which is not billed because of measurement equipment failure or illintentioned and fraudulent manipulation of said equipment. This paper describes new advances that we have developed for Midas project. This project is being developed in the Electronic Technology Department of the University of Seville and its aim is to detect non-technical losses in the database of the Endesa Company. The main symptom of a NTL in a customer is an important drop in his billed energy. Thus, a main task for us is to detect customers with anomalous drops in their consumed energy. Concretely, in the paper we present two new algorithms based on a regression analysis in order to detect two types of patterns of decreasing consumption typical in customers with NTLs.