Domingues, PatricioFrade, MiguelParreira, João Mota2025-10-222025-10-222019-04-10Domingues, P., Frade, M., Parreira, J.M. (2020). Filtering Email Addresses, Credit Card Numbers and Searching for Bitcoin Artifacts with the Autopsy Digital Forensics Software. In: Madureira, A., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). SoCPaR 2018. Advances in Intelligent Systems and Computing, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-17065-3_32.978303017064697830301706532194-5357http://hdl.handle.net/10400.8/14353EISBN - 9783030170653Fonte: https://www.researchgate.net/publication/332330707_Filtering_Email_Addresses_Credit_Card_Numbers_and_Searching_for_Bitcoin_Artifacts_with_the_Autopsy_Digital_Forensics_SoftwareConference name - 10th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2018; Conference date - 13 December 2018 - 15 December 2018; Conference code - 225239Email addresses and credit card numbers found on digital forensic images are frequently an important asset in a forensic casework. However, the automatic harvesting of these data often yields many false positives. This paper presents the Forensic Enhanced Analysis (FEA) module for the Autopsy digital forensic software. FEA aims to eliminate false positives of email addresses and credit card numbers harvested by Autopsy, thus reducing the workload of the forensic examiner. FEA also harvests potential Bitcoin public addresses and private keys and validates them by looking into Bitcoin’s blockchain for the transactions linked to public addresses. FEA explores the report functionality of Autopsy and allows exports in CSV, HTML and XLS formats. Experimental results over four digital forensic images show that FEA eliminates as many as of email addresses and of credit card numbers.engdigital forensicsemail addressescredit card numbersBitcoinconference paper10.1007/978-3-030-17065-3_322194-5365