Hiding in plain sight: criminal network analysis

The communication data are becoming increasingly important for criminal network analysis nowadays, and these data provide a digital trace which can be regarded as a hidden clue to support the crack of criminal cases. Additionally, performing a timely and effective analysis on it can predict criminal intents and take efficient actions to restrain and prevent crimes. The primary work of our research is to suggest an analytical process with interactive strategies as a solution to the problem of characterizing criminal groups constructed from the communication data. It is expected to assist law enforcement agencies in the task of discovering the potential suspects and exploring the underlying structures of criminal network hidden behind the communication data. This process allows for network analysis with commonly used metrics to identify the core members. It permits exploration and visualization of the network in the goal of improving the comprehension of interesting microstructures. Most importantly, it also allows to extract community structures in an appropriate level with the label supervision strategy. Our work concludes illustrating the application of our interactive strategies to a real-world criminal investigation with mobile call logs.

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Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique

Computers ◽

10.3390/computers8010008 ◽

2019 ◽

Vol 8 (1) ◽

pp. 8 ◽

Cited By ~ 7

Author(s):

Marcus Lim ◽

Azween Abdullah ◽

NZ Jhanjhi ◽

Mahadevan Supramaniam

Keyword(s):

Machine Learning ◽

Reinforcement Learning ◽

Network Analysis ◽

Prediction Model ◽

Supervised Learning ◽

Link Prediction ◽

Supervised Machine Learning ◽

Criminal Networks ◽

Criminal Network ◽

Learning Technique

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

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