Anomalous behaviour detection based on heterogeneous data and data fusion

In this paper, we propose a new approach to identify anomalous behaviour based on heterogeneous data and a data fusion technique. There are four types of datasets applied in this study including credit card, loyalty card, GPS, and image data. The first step of the complete framework in this proposed study is to identify the best features for every dataset. Then, the new anomaly detection technique which is recently introduced and known as empirical data analytics (EDA) is applied to detect the abnormal behaviour based on the datasets. Standardised eccentricity (a newly introduced within EDA measure offering a new simplified form of the well-known Chebyshev inequality) can be applied to any data distribution. Image data are processed using pre-trained deep learning network, and classification is done by using support vector machine. Most of the other data used in our previous work are of type “signal”/real number (e.g. credit card, loyalty card and GPS data). However, a clear conclusion that a misuse was made very often cannot be reached based on them only. When gender or age is different from the expected, it is obvious misuse. At the final stage of the proposed method is combining anomaly result and image recognition using data fusion technique. From the experiment results, this proposed technique may simplify the tedious job in the real complex cases of forensic investigation. The proposed technique is using heterogeneous data which combine all the data from the VAST Challenge as well as image data using an introduced data fusion technique. These can assist the human expert in processing huge amount of heterogeneous data to detect anomalies. In future research, text data can also be used as a part of heterogeneous data mixture, and the data fusion technique may be applied to other datasets.