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Novel Approaches for Detecting Frauds in Energy Consumption

Fabris, Fabio and Margoto, Letícia Rosetti and Varejao, Flavio Miguel (2009) Novel Approaches for Detecting Frauds in Energy Consumption. In: 2009 Third International Conference on Network and System Security. IEEE, pp. 546-551. ISBN 978-1-4244-5087-9. E-ISBN 978-0-7695-3838-9. (doi:10.1109/NSS.2009.17) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1109/NSS.2009.17

Abstract

The classification problem is recurrent in the context of supervised learning. A classification problem is a class of computational task in which labels must be assigned to object instances using information acquired from labeled instances of the same type of objects. When these objects contain time sensitive data, special classification methods could be used to take ad- vantage of the inherent extra information. As far as this paper is concerned, the time sensitive data are sequences of values that represent the measured energy consumption of residential clients in a given month. Traditional classifiers do not take temporal features into account, interpreting them as a series of unrelated static information. The proposed method is to develop methods of classification to be applied in a real time-series problem that somehow consider the time series as being the same value being repeatedly measured. Two new approaches are suggested to deal with this problem: the first is a Hybrid classifier that uses clustering, DTW (Dynamic Time Warp) and Euclidean distance to label a given instance. The second is a Weighted Curve Comparison Algorithm that creates consumption profiles and compares them with the unknown instance to classify it.

Item Type: Book section
DOI/Identification number: 10.1109/NSS.2009.17
Uncontrolled keywords: energy consumption; inspection; data mining; clustering algorithms; supervised learning; databases; computer security; computer science; energy measurement; time measurement
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: F. Fabris
Date Deposited: 08 Dec 2013 14:48 UTC
Last Modified: 19 Sep 2019 15:18 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/37380 (The current URI for this page, for reference purposes)
Fabris, Fabio: https://orcid.org/0000-0001-7159-4668
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