Abstract: The sophistication of covert activities employed by criminal networks with technology has been proven to be very challenging for criminal enforcement fraternity to cripple their activities. In view of this, law enforcement agencies need to be equipped with criminal network analysis (CNA) technology which can provide advanced and comprehensive intelligence to uncover the primary members (nodes) and associations (links) within the network. The design of tools to predict links between members mainly rely on Social Network Analysis (SNA) models and machine learning (ML) techniques to improve the precision of the model. The primary challenge of constructing classical ML models such as random forest (RF) with an acceptable level of accuracy is to obtain a large enough dataset to train the model. Obtaining a large enough dataset in the domain of criminal networks is a significant problem due to the stealthy and covert nature of their activities compared to social networks. The main objective of this research is to demonstrate that a link prediction model constructed with a relatively small dataset and dataset generated through self-simulation by leveraging on deep reinforcement learning (DRL) can contribute towards higher precision in predicting links. The training of the model was further fused with metadata (i.e. environment attributes such as criminal records, education level, age and police station proximity) in order to capture the real-life attributes of organised crimes which is expected to improve the performance of the model. Therefore, to validate the results, a baseline model designed without incorporating metadata (CNA-DRL) was compared with a model incorporating metadata (MCNA-DRL).

Marcus Lim, Azween Abdullah and NZ Jhanjhi, “Data Fusion-Link Prediction for Evolutionary Network with Deep Reinforcement Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 11(6), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110644

@article{Lim2020,
title = {Data Fusion-Link Prediction for Evolutionary Network with Deep Reinforcement Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110644},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110644},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {6},
author = {Marcus Lim and Azween Abdullah and NZ Jhanjhi}
}