Cybersecurity Assessment Based on Entropy Weight Method and Polynomial Regression Modeling

Abstract

The aim of this paper is to assess cyber security stability and identify effective strategies. The study collects multi-dimensional data such as crime rate and crime success rate and uses entropy weight method to construct a stability assessment model. On the basis of the traditional entropy weighting method, the weights are set manually to combine domain knowledge and subjective judgment, the normalization anomalies are avoided by checking and fine-tuning the data, random noise is introduced to ensure the stability of the entropy weighting method, and the multi-dimensional comprehensive assessment is carried out and the explanatory nature of the weighted scores is enhanced, which improves the shortcomings of the traditional method. In terms of policy assessment, data related to cybercrime policies are collected and quantified, the crime reduction rate is calculated by differencing, the entropy-weighting method is applied to analyze the effects of prevention, prosecution, and mitigation intensities on the crime reduction rate, and a polynomial regression model is developed to study the effects of policy implementation time, while random fluctuations are added to simulate the rebound for more realistic prediction results. The model was evaluated for goodness-of-fit and cross-validated for stability and good predictive ability. This study provides an effective method for cybersecurity stability assessment and policy formulation.