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Detecting Anomalous Behaviour Using Heterogeneous Data

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 513)

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.

Keywords

Heterogeneous data Anomaly detection RDE Eccentricity 

Notes

Acknowledgments

The first author would like to acknowledge the support from the Ministry of Education Malaysia and Universiti Teknologi MARA, Malaysia for the study grant. The second author would like to acknowledge the New Machine Learning Methods grant from The Royal Society (Grant number IE141329/2014).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Data Science Group, School of Computing and CommunicationLancaster UniversityLancasterUK

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