Cybercrime Detection Using Graph Neural Networks: A Cross-Border Law Enforcement Approach
Abstract
Rapid urbanization and increase in the complexity of urban environment have created substantial problems for the contemporary law enforcement operations in areas, such as crime-prevention, emergency services, crowd management and resource distribution. Traditional policing strategies are often built upon static data analysis and planning, which limits their flexibility regarding adaptability to dynamic and undefined situations in the city. In this research, a proposed simulation framework of digital twin for law enforcement operations in the urban environment that allows real-time situational awareness, predictive analysis and data-driven decision support. The framework unites heterogeneous data sources of urban data, agent based modeling and artificial intelligence, and reinforcement learning to create a synchronous virtual replica of the city that enables replication of the physical operations. Through exhaustive simulations of scenarios, the proposed combination allows some significant improvements in response time, patrol coverage, crime containment rate and utilization efficiency of resources in comparison with conventional methods. Optimization was further enhanced to the stage of enhancing the quality of the services through reinforcement learning to change optimal policing strategies in a virtual environment that is risk-free. The findings indicate the prospective of utilizing digital twins as potent operational planning instruments, policy preparation and assessment in the realism of urban policing. Another theme that the study brings out is the need to ensure the ethical governance, maintenance of privacy and transparency to render adoption accountable.