Theses and Dissertations

Identifying Text File Similarities in Forensic Disk Images Using Fuzzy Logic

Date of Award


Document Type


Degree Name



Computer and Information Science

Committee Chair

Michael Black, Ph.D.


Digital storage is evolving with the growth of technology. Individuals and corporations can access large amounts of digital storage, leaving digital forensics investigators with large amounts of data to collect and analyze in their forensic investigation cases. In addition, analyzing forensic disk images that contain hundreds of thousands of files can cause a problem with time since the investigators’ workloads can vary based on how many cases they are assigned. Fuzzy logic provides a pattern recognition system that could assist in identifying patterns in data. The purpose of this study was to determine if fuzzy logic could reliably aid in identifying similarities between text files for digital forensics investigators analyzing large amounts of data in the preliminary stage of the investigation. In addition, this study was used to determine if fuzzy logic was unreliable in identifying similar text files at the 20% - 60% granularity levels. However, at the 70% - 90% granularity levels, fuzzy logic was reliable in identifying similar text files. Based on these two statements, fuzzy logic was not trustworthy overall since it could not correctly identify similar text files at all granularity levels.

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