Detecting anomalous behaviour using heterogeneous data

Azliza Mohd Ali; Plamen Parvanov Angelov; Xiaowei Gu

doi:10.1007/978-3-319-46562-3_17

Detecting anomalous behaviour using heterogeneous data

Azliza Mohd Ali, Plamen Parvanov Angelov, Xiaowei Gu

Lancaster University

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

7 Citations (Scopus)

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.

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016
Editors	Alexander Gegov, Chrisina Jayne, Qiang Shen, Plamen Angelov
Publisher	Springer Nature
Pages	253-273
Number of pages	21
ISBN (Electronic)	9783319465623
ISBN (Print)	9783319465616
DOIs	https://doi.org/10.1007/978-3-319-46562-3_17
Publication status	Published - 07 Sept 2016
Externally published	Yes

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	513
ISSN (Print)	2194-5357

Keywords

Anomaly detection
Eccentricity
Heterogeneous data
RDE

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10.1007/978-3-319-46562-3_17

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Ali, A. M., Angelov, P. P., & Gu, X. (2016). Detecting anomalous behaviour using heterogeneous data. In A. Gegov, C. Jayne, Q. Shen, & P. Angelov (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016 (pp. 253-273). (Advances in Intelligent Systems and Computing; Vol. 513). Springer Nature. https://doi.org/10.1007/978-3-319-46562-3_17

Ali, Azliza Mohd ; Angelov, Plamen Parvanov ; Gu, Xiaowei. / Detecting anomalous behaviour using heterogeneous data. Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016. editor / Alexander Gegov ; Chrisina Jayne ; Qiang Shen ; Plamen Angelov. Springer Nature, 2016. pp. 253-273 (Advances in Intelligent Systems and Computing).

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title = "Detecting anomalous behaviour using heterogeneous data",

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.",

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author = "Ali, {Azliza Mohd} and Angelov, {Plamen Parvanov} and Xiaowei Gu",

note = "Funding Information: 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). Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.",

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Ali, AM, Angelov, PP & Gu, X 2016, Detecting anomalous behaviour using heterogeneous data. in A Gegov, C Jayne, Q Shen & P Angelov (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016. Advances in Intelligent Systems and Computing, vol. 513, Springer Nature, pp. 253-273. https://doi.org/10.1007/978-3-319-46562-3_17

Detecting anomalous behaviour using heterogeneous data. / Ali, Azliza Mohd; Angelov, Plamen Parvanov; Gu, Xiaowei.
Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016. ed. / Alexander Gegov; Chrisina Jayne; Qiang Shen; Plamen Angelov. Springer Nature, 2016. p. 253-273 (Advances in Intelligent Systems and Computing; Vol. 513).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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N1 - Funding Information: 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). Publisher Copyright: © Springer International Publishing AG 2017.

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Y1 - 2016/9/7

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AB - 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.

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Ali AM, Angelov PP, Gu X. Detecting anomalous behaviour using heterogeneous data. In Gegov A, Jayne C, Shen Q, Angelov P, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016. Springer Nature. 2016. p. 253-273. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-46562-3_17

Detecting anomalous behaviour using heterogeneous data

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