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    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/ISSN › Conference contribution/Paper › peer-review

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Detecting anomalous behaviour using heterogeneous data. / Mohd Ali, Azliza; Angelov, Plamen Parvanov; Gu, Xiaowei.
Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. ed. / Plamen Angelov; Alexander Gegov; Chrisina Jayne; Qiang Shen. Springer, 2016. p. 253-276 (Advances in Intelligent Systems and Computing; Vol. 513).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Mohd Ali, A, Angelov, PP & Gu, X 2016, Detecting anomalous behaviour using heterogeneous data. in P Angelov, A Gegov, C Jayne & Q Shen (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Advances in Intelligent Systems and Computing, vol. 513, Springer, pp. 253-276. https://doi.org/10.1007/978-3-319-46562-3_17

APA

Mohd Ali, A., Angelov, P. P., & Gu, X. (2016). Detecting anomalous behaviour using heterogeneous data. In P. Angelov, A. Gegov, C. Jayne, & Q. Shen (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK (pp. 253-276). (Advances in Intelligent Systems and Computing; Vol. 513). Springer. https://doi.org/10.1007/978-3-319-46562-3_17

Vancouver

Mohd Ali A, Angelov PP, Gu X. Detecting anomalous behaviour using heterogeneous data. In Angelov P, Gegov A, Jayne C, Shen Q, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Springer. 2016. p. 253-276. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-46562-3_17

Author

Mohd Ali, Azliza ; 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, September 7–9, 2016, Lancaster, UK. editor / Plamen Angelov ; Alexander Gegov ; Chrisina Jayne ; Qiang Shen. Springer, 2016. pp. 253-276 (Advances in Intelligent Systems and Computing).

Bibtex

@inproceedings{9eb968d2a4894df08f7b482c76e39f96,
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.",
keywords = "Heterogeneous data , Anomaly detection, RDE , Eccentricity",
author = "{Mohd Ali}, Azliza and Angelov, {Plamen Parvanov} and Xiaowei Gu",
note = "The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_17",
year = "2016",
month = sep,
day = "7",
doi = "10.1007/978-3-319-46562-3_17",
language = "English",
isbn = "9783319465616",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "253--276",
editor = "Plamen Angelov and Alexander Gegov and Chrisina Jayne and Qiang Shen",
booktitle = "Advances in Computational Intelligence Systems",

}

RIS

TY - GEN

T1 - Detecting anomalous behaviour using heterogeneous data

AU - Mohd Ali, Azliza

AU - Angelov, Plamen Parvanov

AU - Gu, Xiaowei

N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_17

PY - 2016/9/7

Y1 - 2016/9/7

N2 - 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.

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.

KW - Heterogeneous data

KW - Anomaly detection

KW - RDE

KW - Eccentricity

U2 - 10.1007/978-3-319-46562-3_17

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

M3 - Conference contribution/Paper

SN - 9783319465616

T3 - Advances in Intelligent Systems and Computing

SP - 253

EP - 276

BT - Advances in Computational Intelligence Systems

A2 - Angelov, Plamen

A2 - Gegov, Alexander

A2 - Jayne, Chrisina

A2 - Shen, Qiang

PB - Springer

ER -