Research on the Global Distribution of Cybercrime Base on Machine Learning and Data Mining
DOI:
https://doi.org/10.62051/5qz0pa22Keywords:
Cybercrime; Cybersecurity Policy; Grey Relational Analysis; Entropy-Weighted TOPSIS; Global Cybersecurity Index Introduction.Abstract
With the acceleration of the global informatization process, cybercrime has increasingly become a serious challenge for the world. The transnational nature of cybercrime makes the response of a single country inadequate, and there is an urgent need for global policy coordination and optimization. Through an in-depth analysis of VCDB data, this paper reveals the distribution characteristics of global cybercrime, and points out the significant differences between developed and developing countries in terms of cybersecurity incident reporting and transparency. Then, use the Grey Relational Analysis method and the five key indicators of the Global Cybersecurity Index (GCI)to evaluate the effectiveness of their national security policies. The results show that technical capability and organizational management are the core factors affecting the effectiveness of cybersecurity policies. In addition, this paper uses the entropy-weighted TOPSIS method to further analyze the relationship between demographic data and network security indicators to verify the previous conclusions. By fostering a unified approach, the international community can better address the evolving landscape of cybercrime and safeguard the digital ecosystem.
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