Quantification of Cyber Crime Based on Entropy Weight Method and ARIMA-DID

Abstract

This paper proposes an indicator assessment model based on the entropy weight method (EWM) and a combined ARIMA-DID analysis framework, focusing on the quantitative algorithm-driven research on the factors influencing the distribution of crime and the effectiveness of policies. Firstly, an assessment system containing core dimensions such as law and technology is constructed, the internal stopping rate (ISR) is introduced as an indicator of law enforcement effectiveness, and the EWM algorithm is used to objectively assign weights to the data from multiple sources, and the indicators related to the level of the economy and the efficiency of law enforcement are identified as the key influencing factors. Second, the ARIMA algorithm is used to model the time series data to capture the crime rate trend, and the DID (double difference method) is used to construct the policy effect assessment model, and typical cases are used to validate the effectiveness of interventions such as “technical defense enhancement” and to confirm the inhibitory effect of the policy on the crime rate. Finally, the Spearman rank correlation algorithm is used to analyze the correlation between the number of crimes and economic and technological characteristics, and to confirm the core influence of the level of economic development. Through the synergy of multiple algorithms, this study provides an algorithm-driven analytical paradigm covering indicator empowerment, trend prediction and policy evaluation in related fields.