Global Trends of Cybercrime and National Cybersecurity Strategy Research

Zekai Deng; Yujing Xu; Wenjie Hu

doi:10.54097/3z28e421

Authors

Zekai Deng
Yujing Xu
Wenjie Hu

DOI:

https://doi.org/10.54097/3z28e421

Keywords:

Cybercrime, Global Trends, Cybersecurity Strategy, Demographic Data

Abstract

With the increasing global internet connectivity, cybercrime has increasingly become a severe challenge faced by the international community. This paper aims to deeply analyze the impact of cybercrime and evaluate the effectiveness of national cybersecurity policies. By collecting and analyzing data on cybercrime incidents from the VERIS Community Database (VCDB), this paper reveals the geographical distribution, type characteristics, and development trends of cybercrime. The study finds that developed countries such as the United States, the United Kingdom, and Australia are the main targets of cybercrime, and there is a significant correlation between the frequency of cybercrime and the demographic data of countries. Based on these findings, this paper puts forward targeted policy recommendations to assist governments of various countries in formulating more effective cybersecurity strategies. This research provides important theoretical and practical bases for understanding the complexity of cybercrime and formulating countermeasures.

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References

[1] Das S, Nayak T. Impact of cybercrime: Issues and challenges [J]. International journal of engineering sciences & Emerging technologies, 2013, 6(2): 142-153.

[2] Erdoğan M, Akmeşe Ö F. Bibliometric Analysis of Studies on Cyber Crimes Between 2000-2023 [J]. ADBA Computer Science, 2025, 2(1): 19-29.

[3] Craigen D, Diakun-Thibault N, Purse R. Defining cybersecurity [J]. Technology innovation management review, 2014, 4(10).

[4] Chinedu P U, Nwankwo W, Masajuwa F U, et al. Cybercrime Detection and Prevention Efforts in the Last Decade: An Overview of the Possibilities of Machine Learning Models [J]. Review of International Geographical Education Online, 2021, 11(7).

[5] Tsakalidis G, Vergidis K, Petridou S, et al. A cybercrime incident architecture with adaptive response policy [J]. Computers & Security, 2019, 83: 22-37.

[6] Georgiadou A, Mouzakitis S, Askounis D. Working from home during COVID-19 crisis: a cyber security culture assessment survey [J]. Security Journal, 2022, 35(2): 486-505.

[7] Sarker I H, Kayes A S M, Badsha S, et al. Cybersecurity data science: an overview from machine learning perspective [J]. Journal of Big data, 2020, 7: 1-29.

[8] Bharadiya J. Machine learning in cybersecurity: Techniques and challenges [J]. European Journal of Technology, 2023, 7(2): 1-14.

[9] Sarker I H. Machine learning for intelligent data analysis and automation in cybersecurity: current and future prospects [J]. Annals of Data Science, 2023, 10(6): 1473-1498.

[10] Yang Z, Fan M, Shao S, et al. Does carbon intensity constraint policy improve industrial green production performance in China? A quasi-DID analysis [J]. Energy Economics, 2017, 68: 271-282.

[11] Jayaweera C D, Aziz N. Reliability of principal component analysis and Pearson correlation coefficient, for application in artificial neural network model development, for water treatment plants [C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2018, 458(1): 012076.