Blockchain-Integrated Cyber Defense Mechanism for Cloud-Driven Financial Applications
DOI:
https://doi.org/10.22399/ijcesen.4331Keywords:
Cloud, Application, Cybersecurity, AIAbstract
On the financial sector, cloud computing has introduced a difference in terms of a scalable innovative and cost-effective service. Nevertheless, it has already caused an increase in cyber security risk - especially when sensitive financial information and compliance are involved. This paper empirically challenges key cybersecurity models of cloud-based financial applications both qualitatively and quantitatively.
Comparing the various risk assessment models, CSA STAR based models are seen to offer 88 percent conformance to the standards lacks any form of economic measurements, as compared to Youssef CSRMF, which offers 90 percent conformance to the standards, and reaches all-time maximum business alignment score of 5/5. The blockchain-based TAB model simulations indicated that overlay-transparency-enhancing models including the TAB Model increase the trustworthiness by factor 4 by reducing the detection latency by 210ms and the detection latency by 480ms.
Moreover, at the application level, layered API security solutions demonstrated significant improvement: a reduction of 120-40 security related incidents monthly in both basic and applied cases along the full-stack using AI/ML surveillance and the detection accuracy changed to 72 to 97. With the study on cost saving in place, installing structures which are based on SME is cheaper than installing hybrid enterprise-level structures which can cost between $200000 and 250000.
The findings indicate that financial managers should consider a hybrid approach to cybersecurity at the expense of such factors as risk evaluation, transparency, and API-layered defenses to maintain the balance between operational efficiency, compliance, and resilience in clouds.
References
Alavizadeh, H., Alavizadeh, H., & Jang-Jaccard, J. (2020). Cyber situation awareness monitoring and proactive response for enterprises on the cloud. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2009.01604 https://arxiv.org/abs/2009.01604
Benabied, S., Zitouni, A., & Djoudi, M. (2015). A cloud security framework based on trust model and mobile agent. A Cloud Security Framework Based on Trust Model and Mobile Agent, 1–8. https://doi.org/10.1109/cloudtech.2015.7336962 A cloud security framework based on trust model and mobile agent | IEEE Conference Publication | IEEE Xplore
Bendicho, C. (2021). Cyber Security in cloud: Risk assessment models. In Lecture notes in networks and systems (pp. 471–482). https://doi.org/10.1007/978-3-030-80119-9_28 https://link.springer.com/chapter/10.1007/978-3-030-80119-9_28
Cheng, M., Qu, Y., Jiang, C., & Zhao, C. (2022). Is cloud computing the digital solution to the future of banking? Journal of Financial Stability, 63, 101073. https://doi.org/10.1016/j.jfs.2022.101073 https://www.sciencedirect.com/science/article/pii/S1572308922000948?via%3Dihub
Cremer, F., Sheehan, B., Fortmann, M., Kia, A. N., Mullins, M., Murphy, F., & Materne, S. (2022). Cyber risk and cybersecurity: a systematic review of data availability. The Geneva Papers on Risk and Insurance Issues and Practice, 47(3), 698–736. https://doi.org/10.1057/s41288-022-00266-6 https://link.springer.com/article/10.1057/s41288-022-00266-6
Das, D., Mohammed, A. S., & Murthy, C. G. (2022, May 17). Application Security Frameworks for Financial APIs in cloud ecosystems: Best practices and solutions. Australian Journal of Machine Learning Research & Applications, 2(1),https://sydneyacademics.com/index.php/ajmlra/article/view/237
Ksibi, S., Jaidi, F., & Bouhoula, A. (2023). A Comprehensive Study of Security and Cyber-Security Risk Management within e-Health Systems: Synthesis, Analysis and a Novel Quantified Approach. Mobile Networks and Applications, 28(1), 107–127. https://doi.org/10.1007/s11036-022-02042-1 https://link.springer.com/article/10.1007/s11036-022-02042-1#citeas
Leila, B., Abdelhafid, Z., & Mahieddine, D. (2017). New framework model to secure cloud data storage. In Advances in intelligent systems and computing,Vol.575,44–52. https://doi.org/10.1007/978-3-319-57141-6_5 New Framework Model to Secure Cloud Data Storage | SpringerLink
Ogety, N. S. S. (2022). Integrating AI for advanced cloud security governance in finance. World Journal of Advanced Research and Reviews, 13(3), 553–562. https://doi.org/10.30574/wjarr.2022.13.3.0122 https://wjarr.com/content/integrating-ai-advanced-cloud-security-governance-finance
Rupra, S. S., & Omamo, A. (2020). A cloud computing security assessment framework for small and medium enterprises. Journal of Information Security, 11(04), 201–224. https://doi.org/10.4236/jis.2020.114014 A Cloud Computing Security Assessment Framework for Small and Medium Enterprises
Xu, R., Li, C., & Joshi, J. (2023). Blockchain-based transparency framework for privacy preserving third-party services. IEEE Transactions on Dependable and Secure Computing, vol. 20, no. 3, pp. 2302-2313. https://doi.org/10.1109/tdsc.2022.3179698 Blockchain-Based Transparency Framework for Privacy Preserving Third-Party Services | IEEE Journals & Magazine | IEEE Xplore
Youssef, A. E. (2020). A framework for cloud security risk management based on the business objectives of organizations. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2001.08993 https://arxiv.org/abs/2001.08993
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
