Author(s)

JiaWei Wen^*, Sai Yang, ChenGxin Zhang

Affiliation(s)

Zijin School of Geology and Mining Fuzhou University, Fuzhou University, Fuzhou 350108, Fujian, China.

Corresponding Author

JiaWei Wen

ABSTRACT

With the acceleration of global digitalization, cybercrime has exhibited transnational and covert characteristics, posing severe challenges to national cybersecurity. To scientifically evaluate the cybersecurity level of various countries and identify effective policy pathways, this study proposes a comprehensive evaluation model integrating the Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution. Based on the five pillars of the International Telecommunication Union Global Cybersecurity Index, an evaluation system comprising 15 sub-indicators is constructed. Indicator weights are determined through expert scoring, and the TOPSIS algorithm is employed to conduct quantitative ranking and grading of the cybersecurity levels of 27 countries. The results show that there is a significant positive correlation between cybersecurity level and comprehensive national strength. Technologically advanced countries such as Finland and the United States are rated as "Excellent", while less developed countries such as Zimbabwe fall into the "Weak" category. Furthermore, Pearson correlation analysis on U.S. cybercrime data and demographic characteristics reveals that internet usage, employment to population ratio of men aged 15-24 and GDP exhibit significant statistical associations with cybercrime incidence. This study provides a data-driven decision-making framework for policymakers to optimize cybersecurity strategies and verifies the core role of synergistic governance of law and technology.

KEYWORDS

AHP-TOPSIS; National cybersecurity level assessment; GCI; Cybercrime prevention and control

CITE THIS PAPER

JiaWei Wen, Sai Yang, ChenGxin Zhang. Ahp-topsis-based assessment framework for cybersecurity policy effectiveness: empirical evidence from gci indicators. Journal of Computer Science and Electrical Engineering. 2025, 7(6): 55-60. DOI: https://doi.org/10.61784/jcsee3089.

REFERENCES

[1] Ashour M, Mahdiyar A. A comprehensive State-of-the-art survey on the recent modified and hybrid analytic hierarchy process Approaches. Applied Soft Computing, 2024, 150: 111014.

[2] Alhakami W. Evaluating modern intrusion detection methods in the face of Gen V multi-vector attacks with fuzzy AHP-TOPSIS. PloS one, 2024, 19(5): e0302559-e0302559.

[3] Wang K, Zhang M, Hong Y, et al. Airborne Network Information Security Risk Assessment Method Based on Improved STPA-TOPSIS. Aerospace, 2025, 12(5): 442-442.

[4] Yunhao Y. A network security situation assessment method based on fusion model. Discover Applied Sciences, 2024, 6(3).

[5] Jianjun Z. A new scoring funcion in multi-criteria decision-making based on vague set. 2024.

[6] Alevizos L, Ta VT. Threat-Informed Cyber Resilience Index: A Probabilistic Quantitative Approach to Measure Defence Effectiveness Against Cyber Attacks. 2024.

[7] Mayra M, Chunming W, Walter F. Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems. Expert Systems With Applications, 2024, 238(PE).

[8] Ramduny J, Kelly C. Connectome-based fingerprinting: Reproducibility, precision, and behavioral Prediction. Neuropsychopharmacology, 2024, 50(1): 114–123.

[9] Iglesias-Martínez ME, Castro-Palacio JC, Scholkmann F, et al. Correlations between Background Radiation inside a Multilayer Interleaving Structure, Geomagnetic Activity, and Cosmic Radiation: A Fourth Order Cumulant-based Correlation Analysis. arXiv, 2020.

[10] Wang J, Zheng N. Measures of Correlation for Multiple Variables. arXiv, 2014(2014).

[11] Ho LH, Lin YL, Chen TY. A Pearson-like correlation-based TOPSIS method with interval-valued Pythagorean fuzzy uncertainty and its application to multiple criteria decision analysis of stroke rehabilitation Treatments. Neural Computing and Applications, 2019, 32(12): 8265–8295.

[12] Zeng S, Luo D, Zhang C, et al. A Correlation-Based TOPSIS Method for Multiple Attribute Decision Making with Single-Valued Neutrosophic Information. International Journal of Information Technology & Decision Making, 2020, 19(01): 343–358.

[13] Jin Y, Wu H, Sun D, et al. A Multi-Attribute Pearson’s Picture Fuzzy Correlation-Based Decision-Making Method. Mathematics, 2019, 7(10): 999.

[14] Zhou Q, Ma Y, Xing Z, et al. Pearson correlation Coefficient-guided large-scale fuzzy cognitive maps learning Algorithm. Fuzzy Sets and Systems, 2025, 519: 109523.

[15] International Telecommunication Union. Global Cybersecurity Index 2024: 5th Edition. Geneva: ITU Publications, 2024: 2.

[16] Bruggemann R, Koppatz P, Scholl M, et al. Global Cybersecurity Index (GCI) and the Role of its 5 Pillars. Social Indicators Research, 2021(prepublish): 1-19.

[17] Subrata C. TOPSIS and Modified TOPSIS: A comparative analysis. Decision Analytics Journal, 2022, 2.