A Study on Cybercrime Policy Analysis Based On K-Means++ Clustering and TOPSIS Modeling

Abstract

As modern technology advances, more and more of our world is interconnected through the Internet, but it has also increased our individual and collective risk of cybercrime. This paper uses modelling to find policies that are effective in curbing cybercrime. Firstly, this article utilized the K-Means++ clustering algorithm to classify countries into five categories based on the cybercrime rates, then this article uses Logistic regression to study the impact of prosecution and reporting on the cybercrime situation. Secondly, in order to explore the model that can identify the effectiveness of relevant policies, this article constructed a Combined Empowerment - TOPSIS model for assessing the effectiveness of the policies and finally get the most effective policies to solve cybercrime in different categories of countries. After that a Pearson correlation analysis of the different policies is performed to explore synergies between policies. Finally, in order to make theory complete and scientific, this article looked for four demographic features: internet coverage (%), wealth (GDP per capita), education level, and population happiness index, predicted the above four indicators as well as the cybercrime rate through the LSTM algorithm, and combined them with the historical data to perform a comprehensive Spearman correlation analysis, and then united the results of the analysis into theory.