Document Type
Article
Publication Date
2016
Subject: LCSH
Cyber forensics, Computer forensics
Disciplines
Computer Engineering | Computer Sciences | Electrical and Computer Engineering | Forensic Science and Technology | Information Security
Abstract
Over the past few years, the popularity of approximate matching algorithms (a.k.a. fuzzy hashing) has increased. Especially within the area of bytewise approximate matching, several algorithms were published, tested, and improved. It has been shown that these algorithms are powerful, however they are sometimes too precise for real world investigations. That is, even very small commonalities (e.g., in the header of a file) can cause a match. While this is a desired property, it may also lead to unwanted results. In this paper, we show that by using simple pre-processing, we significantly can influence the outcome. Although our test set is based on text-based file types (cause of an easy processing), this technique can be used for other, well-documented types as well. Our results show that it can be beneficial to focus on the content of files only (depending on the use-case). While for this experiment we utilized text files, additionally, we present a small, self-created dataset that can be used in the future for approximate matching algorithms since it is labeled (we know which files are similar and how).
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Repository Citation
Jeong, Doowon; Breitinger, Frank; Kang, Hari; and Lee, Sangjin, "Towards Syntactic Approximate Matching-A Pre-Processing Experiment" (2016). Electrical & Computer Engineering and Computer Science Faculty Publications. 60.
https://digitalcommons.newhaven.edu/electricalcomputerengineering-facpubs/60
Publisher Citation
Jeong, D., Breitinger, F., Kang, H. & Lee, Sangjin (2016). Towards Syntactic Approximate Matching-A Pre-Processing Experiment. Journal of Digital Forensics, Security and Law, 11(2), 97-110.
Included in
Computer Engineering Commons, Electrical and Computer Engineering Commons, Forensic Science and Technology Commons, Information Security Commons
Comments
Copyright (c) 2016 Journal of Digital Forensics, Security and Law http://www.jdfsl.org/ This work is licensed under a Creative Commons Attribution 4.0 International License.