Recovery of Deleted Files in the NTFS File System using Python and PyTSK3

Data loss due to accidental or malicious deletion of files poses significant challenges in digital data management and forensics. The ability to efficiently and reliably recover deleted files is crucial for maintaining data integrity, ensuring business continuity, and supporting investigative processes. This paper presents a comprehensive approach to recovering deleted files from NTFS (New Technology File System) using a Python-based tool developed with the PyTSK3 library. The proposed solution systematically traverses the NTFS architecture, specifically targeting the Master File Table (MFT) to identify and restore deleted files while ensuring data integrity and compatibility across various file types. The methodology incorporates robust error handling mechanisms to address issues such as data fragmentation and encrypted files, thereby enhancing the tool's reliability and effectiveness. Experimental evaluations were conducted using multiple NTFS disk images containing a variety of deleted files. The results demonstrate a high recovery success rate of approximately 95%, with the tool outperforming existing models in terms of speed and accuracy. Additionally, the tool's user-friendly interface minimizes the technical expertise required, making data recovery accessible to a broader audience. This research contributes to the field of digital forensics by providing a scalable and efficient solution for NTFS file recovery, with potential applications in data management, legal investigations, and cybersecurity. Future work will focus on expanding the tool's capabilities to support additional file systems and integrating advanced features such as machine learning-based file type classification to further enhance recovery performance.