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Concurrency and Computation Practice and Experience
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Fragments‐Expert: A graphical user interface MATLAB toolbox for classification of file fragments

Authors: Mehdi Teimouri; Zahra Seyedghorban; Fatemeh Amirjani;

Fragments‐Expert: A graphical user interface MATLAB toolbox for classification of file fragments

Abstract

SummaryThe classification of file fragments of various file formats is an essential task in various applications such as firewalls, intrusion detection systems, antiviruses, web content filtering, and digital forensics. However, the community lacks a suitable software tool that can integrate major methods for feature extraction from file fragments and classification among various file formats. In this article, we present Fragments‐Expert that is a graphical user interface MATLAB toolbox for the classification of file fragments. It provides users with 23 categories of features extracted from file fragments. These features can be employed by seven categories of machine learning algorithms for the task of classification among various file formats.

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Keywords

FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Top 10%
Green
bronze