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Enhancing Cybercrime Detection Through Big Data Analytics: A Conceptual Framework

Dharmraj Kumar Vitragi, Lalan Kumar Singh, Arif Mohammad Sattar, Mritunjay Kr. Ranjan


The growing complexity of cybercrime reduction and prevention difficulties necessitates a different approach to dealing with the massive amounts of data involved. The issues of reducing and preventing cybercrime are becoming more complex as cybercrime becomes ingrained in our daily lives. When it comes to showcasing the original division of criminal activities, traditional police operations typically fall short, which means they contribute less to the right deployment of police resources. This study explores strategies for anticipating cybercrime. These techniques include assessing the geological zones that provide a higher level of danger but are outside the scope of standard enforcement powers using Hadoop technology for big data analytics. The method used an algorithm for topographical cybercrime mapping to pinpoint regions of the country with disproportionately high rates of cybercrime. By isolating extreme occurrences of cybercrime clusters, this strategy will shed light on recurring cybercrime trends. Clusters of cybercrime incidents can be used to illustrate common cybercriminal behaviour. The processing capability of the Hadoop platform is a key factor in the enhanced estimation technique. Cybercrime is a global threat because of the increasingly sophisticated tactics criminals employ to breach security systems and steal private information. Recent years have seen promising developments in the use of machine learning, deep learning, and transfer learning for the detection and prevention of cybercrime. Then, we look at transfer learning, early-stage algorithmic cybercrime prediction learning, and active and reinforcement learning. Finally, we synthesise our results and talk about potential future research areas for cybercrime.


Cybercrime reduction, cybercrime clusters, Hadoop, police data, methods, algorithm, machine learning, deep learning, transfer learning

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