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@article{178307,
        author = {Mr. B.venugopal and Ms. Nihal Baba},
        title = {Metadata Analyzer using MERN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {7103-7111},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178307},
        abstract = {Metadata plays a crucial role in digital forensics, cybersecurity, and data integrity verification, as it contains essential file properties such as timestamps, author details, and embedded information However, metadata can be manipulated, hidden, or altered, posing security risks in data authenticity and forensic investigation. The primary challenge in metadata analysis lies in detecting anomalies and inconsistencies caused by unauthorized modifications. Traditional rule-based approaches often generate false positives, making it difficult to differentiate between legitimate and suspicious metadata alteration. To address this, we developed Meta Data Analyzer (MDA), a MERN stack-based web application that allows users to upload files, extract metadata, and detect anomalies using machine learning algorithms. Our system integrates One-Class SVM and Isolation Forest models to enhance detection accuracy. These models are particularly well-suited for unsupervised anomaly detection in high-dimensional data, such as file metadata, where labeled examples of tampered files are scarce. Findings from our implementation show that ML-based anomaly detection significantly reduces false positives and improves file integrity verification. The system also provides a user-friendly interface for real-time metadata analysis, making it a valuable tool for forensic experts and security analysts. Future enhancements include deep learning-based anomaly detection and real-time monitoring for enterprise-level security applications, which would further improve the scalability and robustness of the system.},
        keywords = {MERN | UI | API | JSON | DF | PST | EXIF | CSV | PDF | SQL},
        month = {May},
        }