Detecting Anomalous Behaviour Using Heterogeneous Data

Ali, Azliza Mohd; Angelov, Plamen; Gu, Xiaowei

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

Cited by 6 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed EDA-based anomaly detection approach has been applied to a set of a credit card, loyalty card, and GPS datasets (Mohd Ali et al 2016). Anomalous behaviour has been detected in the credit card spending pattern.…”

Section: Results and Analysismentioning

confidence: 99%

“…The abnormal value can be determined by the value of n we set in the dataset when calculating the ε. For example in these data, we consider the value of ε = 26 which corresponds to 5σ according to the Chebyshev inequality (Mohd Ali et al 2016)…”

Section: Data Fusionmentioning

confidence: 99%

“…Longitude and latitude can produce information about the mobility of staff members such as the projection of the trajectory, average speed, ratio of the trajectory angle, and distance. The formula to calculate all of these features can be found in Mohd Ali et al (2016). A projection of the trajectory can be calculated from the trajectory of the staff members from starting point to the ending point.…”

Section: Feature Extractions (A) Credit Card and Loyalty Card Datamentioning

confidence: 99%

See 2 more Smart Citations

Anomalous behaviour detection based on heterogeneous data and data fusion

Ali

Angelov

2018

Soft Comput

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Results and Analysismentioning

confidence: 99%

Section: Data Fusionmentioning

confidence: 99%

Section: Feature Extractions (A) Credit Card and Loyalty Card Datamentioning

confidence: 99%

See 1 more Smart Citation

Anomalous behaviour detection based on heterogeneous data and data fusion

Ali

Angelov

2018

Soft Comput

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this paper, we consider the case study from the VAST Challenge 2014. The proposed method in [1] applied 3 types of data from the VAST Challenge data. This study discovered anomalous behaviour in spending based on credit card data but not in the loyalty card data.…”

Section: A Vast Challenge 2014mentioning

confidence: 99%

“…Automatic detection plays an important role in detecting anomalous behaviour in data and can benefit in preventing crime. The system described in [1] may create an alert or signal if there is an anomalous behaviour. Anomalous behaviour can take place in public places such as airports, subway stations or shopping malls; instances are incidents of suicide bombing in Brussels Airport and Ataturk Airport, Turkey in 2016 and the recent London attack.…”

Section: Introductionmentioning

confidence: 99%

Gender and Age Classification of Human Faces for Automatic Detection of Anomalous Human Behaviour

Ali

Angelov

2017

2017 3rd IEEE International Conference on Cybernetics (CYBCONF)

Self Cite

View full text Add to dashboard Cite

In this paper, we introduce an approach to classify gender and age from images of human faces which is an essential part of our method for autonomous detection of anomalous human behaviour. Human behaviour is often uncertain, and sometimes it is affected by emotion or environment. Automatic detection can help to recognise human behaviour which later can assist in investigating suspicious events. Central to our proposed approach is the recently introduced transfer learning. It was used on the basis of deep learning and successfully applied to image classification area. This paper is a continuous study from previous research on heterogeneous data in which we use images as supporting evidence. We present a method for image classification based on a pretrained deep model for feature extraction and representation followed by a Support Vector Machine classifier. Because very few data sets with labels of gender and age exist of face images, we build one dataset named GAFace and applied our proposed method to this dataset achieving excellent results and robustness (gender classification: 90.33% and age classification: 80.17% accuracy) approaching human performance.

show abstract