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Detecting Anomalous Behaviour Using Heterogeneous Data

Ali, Azliza Mohd, Angelov, Plamen, Gu, Xiaowei (2016) Detecting Anomalous Behaviour Using Heterogeneous Data. Advances in Computational Intelligence Systems, 513 . pp. 253-273. ISSN 2194-5357. (doi:10.1007/978-3-319-46562-3_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) (KAR id:90105)

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
https://doi.org/10.1007/978-3-319-46562-3_17

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.

Item Type: Article
DOI/Identification number: 10.1007/978-3-319-46562-3_17
Uncontrolled keywords: Heterogeneous data; Anomaly detection; RDE; Eccentricity
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 09 Sep 2021 13:01 UTC
Last Modified: 10 Sep 2021 10:15 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90105 (The current URI for this page, for reference purposes)
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