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Dataset for file fragment classification of textual file formats

Fatemeh Mansouri Hanis¹, Mehdi Teimouri²

¹Information Theory and Coding Laboratory, University of Tehran, Tehran, Iran.

BMC Research Notes

|December 13, 2019

Summary

This study introduces a new dataset for classifying textual file fragments, crucial for network forensics. The dataset aids researchers in comparing file fragment classification methods across various formats and languages.

Area of Science:

Computer Science
Digital Forensics

Background:

Textual file format classification is vital in network forensics.
Existing datasets lack file fragments, hindering method comparison.
A standardized dataset is needed for evaluating file fragment classification techniques.

Purpose of the Study:

To introduce a novel dataset for textual file format fragments.
To facilitate comparative analysis of file fragment classification methods.
To support research in network forensics and data analysis.

Main Methods:

A dataset comprising 22,500 file fragments was created.
Fragments represent five textual file formats: Word 97-2003 Binary, OOXML, PDF, RTF, and TXT.
Data includes fragments in English, Persian, and Chinese.

Main Results:

The dataset provides a standardized resource for research.
Enables direct comparison of classification algorithms.
Addresses the gap in publicly available file fragment data.

Conclusions:

The new dataset is essential for advancing textual file fragment classification.
It will accelerate research and development in network forensics.
Facilitates reproducible research and method validation.

Keywords:

Classification File formats File fragments Textual file formats

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