| Issue |
SHS Web Conf.
Volume 231, 2026
7th International Symposium on Frontiers of Economics and Management Science (FEMS 2026)
|
|
|---|---|---|
| Article Number | 01011 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/shsconf/202623101011 | |
| Published online | 19 May 2026 | |
Cybercrime: The Dark Cloud Over Our Online Lives - Chase It Away
International College, China Agricultural University, Beijing 100080, China.
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Cybercrime has become a pressing global issue with significant societal consequences. This study develops a comprehensive framework to analyze cybercrime patterns and evaluate the effectiveness of cybersecurity policies through data-driven approaches.We first establish a Cybercrime Distribution model to examine global patterns of cybercrime incidence and vulnerability. The model integrates multiple cybersecurity indices and demographic factors to identify high-risk regions and temporal trends. Our analysis reveals important relationships between national characteristics and cybercrime susceptibility. Next, we propose a Cybersecurity Policy Evaluation framework that combines predictive modeling with comparative analysis. This innovative approach allows for systematic assessment of various policy types, identifying which categories demonstrate the greatest impact on reducing cybercrime rates. The results provide clear insights into policy effectiveness across different cybersecurity domains. Furthermore, we investigate correlations between socioeconomic factors and cybercrime prevalence. Our findings offer valuable perspectives on how demographic characteristics influence both cybercrime rates and national cybersecurity preparedness. The study concludes with practical recommendations for policymakers, derived from rigorous model validation and sensitivity analysis. Our framework provides a robust foundation for understanding cybercrime dynamics and formulating effective defense strategies.This research employs advanced statistical methodologies and machine learning algorithms to process large-scale cybersecurity datasets. The analytical framework incorporates both qualitative and quantitative measures, enabling a more nuanced understanding of cyber threat landscapes. By examining various attack vectors and their corresponding mitigation strategies, the study identifies critical gaps in current cybersecurity infrastructures. Additionally, the research highlights the importance of international cooperation in addressing cross-border cyber threats, emphasizing the need for standardized metrics and shared intelligence resources.
Key words: Cybersecurity policy / Cybercrime analysis / Predictive modeling
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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