Integrity Verification in Decentralized File Storage Systems in Real-Time with AI
Keywords:
Artificial Intelligence, decentralized storage, file integrity, decentralized file systemsAbstract
Cloud storage is replaced by IPFS and Filecoin which provide security, efficiency, and independence. Decentralised systems have unstable nodes that compromises the files and its data. Scalability and real-time performance is limited by the hash-based file integrity verification. Decentralised file storage dependability improve the AI integrity verification in anomaly detection.
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References
Zhang, Y., & Liu, S. (2020). Machine learning for integrity verification in decentralized systems. IEEE Transactions on Network and Service Management, 17(4), 1-13. https://doi.org/10.1109/TNSM.2020.3029204
Sharma, P., & Gupta, A. (2021). Real-time anomaly detection in decentralized storage networks. International Journal of Computer Science and Network Security, 21(3), 78-85. https://doi.org/10.1109/IJCNS.2021.2978245
Patel, R., & Kumar, S. (2020). AI-based anomaly detection for decentralized file systems. Journal of Blockchain Research, 5(1), 42-52. https://doi.org/10.1109/JBR.2020.041050
S. Kumari, “Kanban and AI for Efficient Digital Transformation: Optimizing Process Automation, Task Management, and Cross-Departmental Collaboration in Agile Enterprises”, Blockchain Tech. & Distributed Sys., vol. 1, no. 1, pp. 39–56, Mar. 2021
Sivaraman, Hariprasad. (2020). Integrating Large Language Models for Automated Test Case Generation in Complex Systems.
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.
S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021
S. Kumari, “Kanban and Agile for AI-Powered Product Management in Cloud-Native Platforms: Improving Workflow Efficiency Through Machine Learning-Driven Decision Support Systems”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 867–885, Aug. 2019
S. Kumari, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021
Sivaraman, Hariprasad. (2020). Intelligent Deployment Orchestration Using ML for Multi-Environment CI/CD Pipelines.
S. Kumari, “AI-Powered Cybersecurity in Agile Workflows: Enhancing DevSecOps in Cloud-Native Environments through Automated Threat Intelligence ”, J. Sci. Tech., vol. 1, no. 1, pp. 809–828, Dec. 2020.
S. Kumari, “Cloud Transformation and Cybersecurity: Using AI for Securing Data Migration and Optimizing Cloud Operations in Agile Environments”, J. Sci. Tech., vol. 1, no. 1, pp. 791–808, Oct. 2020.
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
Sivaraman, Hariprasad. (2021). INTELLIGENT AUTOMATION FOR SERVICE DEGRADATION PREDICTION USING LLMS AND OBSERVABILITY DATA. International Journal of Engineering Management. 6. 10.5281/zenodo.14342920.
S. Kumari, “AI-Powered Cloud Security for Agile Transformation: Leveraging Machine Learning for Threat Detection and Automated Incident Response ”, Distrib Learn Broad Appl Sci Res, vol. 6, pp. 467–488, Oct. 2020
Mehta, S., & Singh, K. (2021). Securing decentralized storage through AI-driven security models. Journal of Cybersecurity and Information Integrity, 12(2), 112-118. https://doi.org/10.1016/JCI.2021.05.003
Choi, J., & Lee, D. (2021). Integrating AI and blockchain for secure file storage. IEEE Transactions on Blockchain Technology, 8(4), 10-20. https://doi.org/10.1109/TBC.2021.3021502
Patel, S., & Kumar, V. (2020). AI-enhanced blockchain for decentralized file integrity verification. Journal of Blockchain Applications, 9(2), 125-134. https://doi.org/10.1109/JBA.2020.2928799
Singh, P., & Bansal, A. (2021). Optimizing machine learning models for decentralized networks. IEEE Journal of Computational Intelligence, 16(3), 200-212. https://doi.org/10.1109/JCI.2021.3013456
Sharma, A., & Gupta, V. (2021). Privacy-preserving AI models in decentralized systems. International Journal of AI and Data Privacy, 8(2), 56-67. https://doi.org/10.1109/IJAI.2021.3064311
Lee, H., & Lim, C. (2020). Enhancing blockchain security using machine learning. Journal of Distributed Ledger Technology, 10(1), 90-100. https://doi.org/10.1109/JDLT.2020.3156795
Raghavan, P., & Kumar, A. (2020). Deep learning for anomaly detection in decentralized networks. IEEE Transactions on Network Security, 18(4), 345-358. https://doi.org/10.1109/TNS.2020.312132