Abstract

ObjectivesClassification of textual file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with textual formats. Therewith, there is no public dataset for file fragments of textual file formats. So, a big research challenge in file fragment classification of textual file formats is to compare the performance of the developed methods over the same datasets.Data descriptionIn this study, we present a dataset that contains file fragments of five textual file formats: Binary file format for Word 97–Word 2003, Microsoft Word open XML format, portable document format, rich text file, and standard text document. This dataset contains the file fragments in three different languages: English, Persian, and Chinese. For each pair of file format and language, 1500 file fragments are provided. So, the dataset of file fragments contains 22,500 file fragments.

Highlights

  • Many researches have been carried in the field of file fragment classification of textual file formats [1–6]

  • Therewith, there is no public dataset for file fragments of textual file formats

  • We present a dataset that contains file fragments of five textual file formats: Binary file format for Word 97–Word 2003 (DOC), Microsoft Word open XML format (DOCX), portable document format (PDF), rich text file (RTF), and standard text document (TXT)

Read more

Summary

Introduction

Many researches have been carried in the field of file fragment classification of textual file formats [1–6]. There are a few publicly available datasets of files with different formats [7]. Therewith, there is no public dataset for file fragments of textual file formats. We present a dataset that contains file fragments of five textual file formats: Binary file format for Word 97–Word 2003 (DOC), Microsoft Word open XML format (DOCX), portable document format (PDF), rich text file (RTF), and standard text document (TXT). This dataset includes the file fragments in three different languages: English (EN), Persian (FA), and Chinese (CH).

Objectives
Results
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.