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  • UKCI2016_Azliza_CameraReady

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_17

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Detecting anomalous behaviour using heterogeneous data

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
Publication date7/09/2016
Host publicationAdvances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK
EditorsPlamen Angelov, Alexander Gegov, Chrisina Jayne, Qiang Shen
PublisherSpringer
Pages253-276
Number of pages24
ISBN (Electronic)9783319465623
ISBN (Print)9783319465616
Original languageEnglish

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume513
ISSN (Print)2194-5357

Abstract

In this paper, we propose a method to detect anomalous behaviour using heterogenous data. This method detects anomalies based on the recently introduced approach known as Recursive Density Estimation (RDE) and the so called eccentricity. This method does not require prior assumptions to be made on the type of the data distribution. A simplified form of the well-known Chebyshev condition (inequality) is used for the standardised eccentricity and it applies to any type of distribution. This method is applied to three datasets which include credit card, loyalty card and GPS data. Experimental results show that the proposed method may simplify the complex real cases of forensic investigation which require processing huge amount of heterogeneous data to find anomalies. The proposed method can simplify the tedious job of processing the data and assist the human expert in making important decisions. In our future research, more data will be applied such as natural language (e.g. email, Twitter, SMS) and images.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_17