Blockchain-Enabled Secure Management Information Systems for Data Integrity and Cyber Resilience
DOI:
https://doi.org/10.61424/fym22988Keywords:
Blockchain Technology, Management Information Systems, Data Integrity, Cyber Resilience, Decentralized SecurityAbstract
This study proposes a blockchain-enabled MIS framework designed to enhance data integrity, strengthen cyber resilience, and improve system availability. The performance evaluation demonstrates that blockchain integration significantly improves data integrity compared to traditional and partially secured systems. While conventional MIS architectures achieve approximately 70% integrity, secured systems improve this to 82%. In contrast, blockchain-enabled MIS achieves the highest integrity level of approximately 95%, due to its decentralized and immutable ledger structure. This ensures that data modifications are transparent and tamper-resistant. The study further examines cyberattack resistance across different system architectures. Blockchain-enabled MIS shows substantial improvements in resisting distributed denial-of-service (DDoS) attacks, data tampering, and unauthorized access, achieving resistance levels between 85% and 90%, compared to 55%–60% in traditional systems. These results highlight the effectiveness of decentralized architectures in mitigating security threats. Additionally, the analysis of system downtime reveals that blockchain-based systems significantly reduce operational disruptions. Centralized systems experience approximately 120 minutes of downtime per month, while secured systems reduce this to 80 minutes. Blockchain-enabled systems achieve the lowest downtime of approximately 35 minutes, demonstrating improved resilience and reliability. Overall, the findings indicate that integrating blockchain technology into MIS architectures provides a robust solution for enhancing data integrity, security, and system performance. The proposed framework offers significant potential for application in critical sectors, including finance, healthcare, and enterprise systems, where data reliability and cybersecurity are essential.
References
Ahmad, A., Imran, M., & Ahsan, H. (2023). Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases. Pharmaceutics, 15(6), 1630. https://doi.org/10.3390/pharmaceutics15061630
Ahsan, H. (2019). Biomolecules and biomarkers in oral cavity: bioassays and immunopathology. Journal of Immunoassay and Immunochemistry, 40(1), 52-69.
Alam, M. I., Hemal, M. A. K. P., Sami, M. A., & Rahman, M. L. (2024). Robust and Interpretable Crop Recommendation: A Case Study on a Balanced Multi-crop Agronomic Dataset. European Journal of Ecology, Biology and Agriculture, 1(5), 168-184. https://doi.org/10.59324/ejeba.2024.1(5).14
Alam, M. I., Sami, M. A., Al Masud, A., Ahmed, H., & Hossain, F. (2025). AI-Driven Big Data Analytics for Personalized Cancer Treatment: Integrating Multi-Omics, Medical Imaging, and Predictive Intelligence. Journal of Computer Science and Technology Studies, 7(11), 428-441. https://doi.org/10.32996/jcsts.2025.7.11.40
Alam, M. I., Sami, M. A., Hemal, M. A. K. P., & Rahman, M. L. (2023). Predictive Analytics and Decision Intelligence for Climate-Resilient Agritech Systems. Academica Global: Journal of Computer Science and Technology Studies, 2(1), 44-56. https://doi.org/10.32996/agjcsts.2023.2.1.4
Aryutova, K., Stoyanov, D. S., Kandilarova, S., Todeva-Radneva, A., & Kostianev, S. S. (2021). Clinical use of neurophysiological biomarkers and self-assessment scales to predict and monitor treatment response for psychotic and affective disorders. Current Pharmaceutical Design, 27(39), 4039-4048.
Atzei, N., Bartoletti, M., & Cimoli, T. (2017). A survey of attacks on Ethereum smart contracts. International Conference on Principles of Security and Trust.
Casino, F., Dasaklis, T. K., & Patsakis, C. (2019). A systematic literature review of blockchain-based applications. Telematics and Informatics, 36, 55–81.
Das, N., Sultana, S., Sikder, M. S., Himel, H. U., Saha, U. S., & Rashed, R. A. M. (2025). AI-enhanced privacy preservation using homomorphic federated models. 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT), 1–8. https://doi.org/10.1109/ICAFT66710.2025.11453096
Dennis, J. K., Sealock, J. M., Straub, P., Lee, Y. H., Hucks, D., Actkins, K. E., ... & Davis, L. K. (2021). Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease. Genome medicine, 13(1), 6.
Dhama, K., Latheef, S. K., Dadar, M., Samad, H. A., Munjal, A., Khandia, R., ... & Joshi, S. K. (2019). Biomarkers in stress related diseases/disorders: diagnostic, prognostic, and therapeutic values. Frontiers in molecular biosciences, 6, 465402.
Hassan, J., Barikdar, C. R., Hasan, S. N., Kaur, J., Chakraborty, P., & Miah, M. A. (2025). Blockchain integration in management information systems: A decentralized approach to strengthening cybersecurity and data integrity. 2025 5th International Conference on Electrical, Computer and Energy Technologies (ICECET), 1–7. https://doi.org/10.1109/ICECET63943.2025.11472020
Hemal, M. A. K. P., Sayeed, N., Sami, M. A., Alam, M. I., Sikder, T. R., Dipa, S. A., & Rahman, M. L. (2025). Leveraging Data Analytics to Strengthen Public Health and Global Economic Sustainability. European Journal of Medical and Health Research, 3(4), 253- 263. https://doi.org/10.59324/ejmhr.2025.3(4).37
Nusrat, S., Hossain, F., & Sikder, T. R. (2024). Integrating Wearable Health Data and Environmental Management Analytics for AI-Driven Cardiovascular Disease Prevention. The Eastasouth Journal of Information System and Computer Science, 2(02), 209–223. https://doi.org/10.58812/esiscs.v2i02.868
Orthi, S. M., Sikder, T. R., Uddin, S. M. M., Roy, T., Hossain, M. J., & Faruk, M. I. (2025). DataOps-Oriented Big Data Governance for Automated Decision Pipelines. 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT), Belagavi, India, 2025, pp. 1-8. https://doi.org/10.1109/ICAFT66710.2025.11452860.
Sami, M. A., Hemal, M. A. K. P., Alam, M. I., & Rahman, M. L. (2024). Data Governance and Analytics Infrastructure for Scalable Decision-Making in Development and Agritech Programs. European Journal of Applied Science, Engineering and Technology, 2(2), 388-403. https://doi.org/10.59324/ejaset.2024.2(2).28
Sikder, T. R., Dash, S., Uddin, B., & Hossain, F. (2023a). AI-Powered Data Analytics and Multi-Omics Integration for Next-Generation Precision Oncology and Anticancer Drug Development. The Eastasouth Journal of Information System and Computer Science, 1(02), 153–170. https://doi.org/10.58812/esiscs.v1i02.838
Sikder, T. R., Sayeed, N., Hossain, M. J., Faruk, M. I., Alam, M. I., Uddin, S. M. M., & Adnan, M. (2025). AI-Driven Environmental Precision Oncology: Integrating Big Data, Multi-Omics, Medical Imaging, and Exposomic Intelligence for Personalized Cancer Care. International Journal of Computational and Experimental Science and Engineering, 11(4). https://doi.org/10.22399/ijcesen.4533
Sikder, T. R., Siam, M. A., Melon, M. M. H., Uddin, S. M. M., Mohonta, S. C., & Karim, F. (2023b). A Multimodal Data Analytics Framework for Early Cancer Detection Using Genomic, Radiomic, and Clinical Big Data Fusion. Journal of Computer Science and Technology Studies, 5(3), 183-188. https://doi.org/10.32996/jcsts.2023.5.3.13
Silberring, J., & Ciborowski, P. (2010). Biomarker discovery and clinical proteomics. TrAC Trends in Analytical Chemistry, 29(2), 128-140.
Uddin, S. M. M., Chy, M. A. R., Sikder, T. R., Faruk, M. I., Adnan, M., & Hossain, M. J. (2025). Bio-Cognitive AI Systems for Predictive Healthcare Decision Support. 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT), Belagavi, India, 2025, pp. 1-9. https://doi.org/10.1109/ICAFT66710.2025.11453175.
Vanu, N., Hasan, M. R., Sikder, T. R., & Tamanna, Z. S. (2021). AI-Driven Big Data Analytics for Precision Medicine: A Unified Framework Integrating Molecular Data Intelligence, Wearable Health Systems, and Predictive Modeling. Journal of Computer Science and Technology Studies, 3(2), 124-141. https://doi.org/10.32996/jcsts.2021.3.2.11
Xu, X., Weber, I., & Staples, M. (2019). Architecture for blockchain applications. Springer.
Yusuf, M. A., Chowdhury, N. M., Rone, P. D., Saha, P. P., Hossan, M. I., Sarkar, D., Paul, R., Hossain, M. R., & Chakraborty, M. (2025). Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification. EAI Endorsed Trans AI Robotics. 4.1-12. https://doi.org/10.4108/airo.9785. Available from: https://publications.eai.eu/index.php/airo/article/view/978z5
Yusuf, M. A., Khan, M. R. K., Saha, P. P., & Rahaman, M. M. (2024). Data fusion of semantic and depth information in the context of object detection. In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) (pp. 1124–1129). https://doi.org/10.1109/ICoICI62503.2024.10696627.
Zheng, Z., Xie, S., Dai, H., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352–375.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nirupam Khan, Mennon Karim, Rashid Alam, Raisul Khan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
