EvidenceX: A Comprehensive Digital Forensics Tool for Evidence Extraction
DOI:
https://doi.org/10.32628/CSEIT25112859Keywords:
Digital Forensics, Metadata Extraction, Anomaly Detection, Activity Log Analysis, Privacy ProtectionAbstract
Background: Digital forensics operates as a core investigative element in present times since analysts need to examine metadata and prove file authenticity while locating hidden information. High-end file systems such as XFS and Btrfs include sophisticated features for journaling and copy-on-write that complicate the tasks of data forensic recovery as well as examination processes. Modern forensic tools focus on retrieving information but they demonstrate limited capability in producing extensive metadata analysis together with timeline reports. Investigative teams gain better results from checking times- tamps along with source device IDs and file historical logs to prove evidence authenticity compared to simply using deleted file recovery methods. File integrity remains secure because cryptographic hash values function as digital identification marks for authentication validation. Supplies of digital content with con- cealed data require identification to retrieve vital incriminating evidence. The modern forensic requirements have their solution in Evi- denceX which represents a state-of-the-art digital forensic utility. The system performs metadata extraction from multiple file types while automatically finding concealed content and analyzes both system logs and unusual behavior patterns simultaneously with steganography detection capabilities. Anomaly detection functionality in the system protects data consistency while the ”de-forensic” innovation allows secure metadata removal for privacy support. The present document examines how EvidenceX functions and demonstrates its operational value to boost digital forensic procedures through improved accuracy alongside effi- ciency and reliability aspects.
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References
Skluzacek, T. J., Chard, K., Foster, I. (2022, October). Automated metadata extraction: challenges and opportunities. In 2022 IEEE 18th International Conference on e-Science (e-Science) (pp. 495-500). IEEE.
Li, W., Susilo, W., Xia, C., Huang, L., Guo, F., Wang, T. (2024). Secure data integrity check based on verified public key encryption with equality test for multi-cloud storage. IEEE transactions on dependable and secure computing, 21(6), 5359-5373.
Mandal, P. C., Mukherjee, I., Paul, G., Chatterji, B. N. (2022). Digital image steganography: A literature survey. Information sciences, 609, 1451-1488.
Kathiravan, M., Logeshwari, R., Pavithra, S., Meenakshi, M., Durga, V. S., Vijayakumar, M. (2023, February). A cloud based improved file handling and duplicate removal using md5. In 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 1532-1536). IEEE.
Hameed, M. A., Hassaballah, M., Abdelazim, R., Sahu, A. K. (2024). A novel medical steganography technique based on adversarial neural cryptography and digital signature using least significant bit replacement. International Journal of Cognitive Computing in Engineering, 5, 379- 397.
Kaur, H., Kumar, M. (2023). Signature identification and verification techniques: state-of-the-art work. Journal of Ambient Intelligence and Humanized Computing, 14(2), 1027-1045.
Kohm, V. N. (2024). Optimizing Metadata Handling with vkFS: A Hybrid Key-Value Store File System leveraging RocksDB (Doctoral dissertation, Vrije Universiteit Amsterdam).
Ulrich, H., Kock-Schoppenhauer, A. K., Deppenwiese, N., Go¨tt, R., Kern, J., Lablans, M., ... Ingenerf, J. (2022). Understanding the nature of metadata: systematic review. Journal of medical Internet research, 24(1), e25440.
Wang, Y., Yu, Y., Yang, W., Guo, L., Chau, L. P., Kot, A. C., Wen, B. (2023). Raw image reconstruction with learned compact metadata In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 18206-18215).
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