Detecting anomalous behaviour using heterogeneous data

Azliza Mohd Ali, Plamen Parvanov Angelov, Xiaowei Gu

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

7 Citations (SciVal)


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 languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 16th UK Workshop on Computational Intelligence, 2016
EditorsAlexander Gegov, Chrisina Jayne, Qiang Shen, Plamen Angelov
PublisherSpringer Nature
Number of pages21
ISBN (Electronic)9783319465623
ISBN (Print)9783319465616
Publication statusPublished - 07 Sept 2016
Externally publishedYes

Publication series

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


  • Anomaly detection
  • Eccentricity
  • Heterogeneous data
  • RDE


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