Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46562-3_17
Accepted author manuscript, 810 KB, PDF document
Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
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 -