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@article{200465,
        author = {ANURAG TYAGI and Atul Saini and Ayush Garg and Ayush Gupta and Priyanka},
        title = {Detecting Suspicious file migration in Cloud},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2635-2640},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200465},
        abstract = {The cloud computing has transformed the manner in which organizations store and share data and typical file migrations within the distributed environment have increased the challenge of security. Unauthorized or illegal file transfers can be indicators of the untimely insider abuse or even the attack on the data. As India continues to go high in digitization of educational and cultural knowledge, the integrity of the digital property should be ensured as a compulsory requirement. This survey will seek to examine the literature of suspicious file migration detection in cloud ecosystems. It also talks about rule-based approaches, machine learning and blockchain- based audit models which enhance the quantity of ac- countability and the trustworthiness of handling electronic information. Each of the approaches is characterized in accuracy, scalability and efficiency terms that reveal that the system has persistent flaws, such as high false-positives and lack of real-time monitoring. The study emphasizes the need to have lightweight and flexible systems that integrate unmonitored learning systems with safe logging systems to store and protect digital assets. The file migration detection, as applied to suspicious files, would be of the utmost importance in the real-life scenario in education, healthcare, finance and government process where sensitive information is exchanged between virtual servers almost on a continuous basis. To ensure the integrity of the digital resources, real-time detection systems of anomalies are applied to ensure confidentiality and reliability of the digital resources to prevent data leakage or unauthorized access. Such solutions will be a long way in improving institutional data governance, boost compliance with security rules and trust in mega digital transformation programmes amongst users.},
        keywords = {Blockchain, Data Preservation, Anomaly Detection, Machine Learning, Security.},
        month = {May},
        }