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NapierOne: A modern mixed file data set alternative to Govdocs1
- Source :
- Forensic Science International: Digital Investigation, Volume 40, 2022, 301330, ISSN 2666-2817
- Publication Year :
- 2022
-
Abstract
- It was found when reviewing the ransomware detection research literature that almost no proposal provided enough detail on how the test data set was created, or sufficient description of its actual content, to allow it to be recreated by other researchers interested in reconstructing their environment and validating the research results. A modern cybersecurity mixed file data set called NapierOne is presented, primarily aimed at, but not limited to, ransomware detection and forensic analysis research. NapierOne was designed to address this deficiency in reproducibility and improve consistency by facilitating research replication and repeatability. The methodology used in the creation of this data set is also described in detail. The data set was inspired by the Govdocs1 data set and it is intended that NapierOne be used as a complement to this original data set. An investigation was performed with the goal of determining the common files types currently in use. No specific research was found that explicitly provided this information, so an alternative consensus approach was employed. This involved combining the findings from multiple sources of file type usage into an overall ranked list. After which 5000 real-world example files were gathered, and a specific data subset created, for each of the common file types identified. In some circumstances, multiple data subsets were created for a specific file type, each subset representing a specific characteristic for that file type. For example, there are multiple data subsets for the ZIP file type with each subset containing examples of a specific compression method. Ransomware execution tends to produce files that have high entropy, so examples of file types that naturally have this attribute are also present.
- Subjects :
- Computer Science - Cryptography and Security
Subjects
Details
- Database :
- arXiv
- Journal :
- Forensic Science International: Digital Investigation, Volume 40, 2022, 301330, ISSN 2666-2817
- Publication Type :
- Report
- Accession number :
- edsarx.2201.08154
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1016/j.fsidi.2021.301330