Nowadays, the speed up development and use of digital devices such as
smartphones have put people at risk of internet crimes. The evidence of present
crimes in a computer file can be easily unreachable by changing the prefix of a
file or other algorithms. In more complex cases, either file divided into
different parts or the parts of a file that has information about the file type
are deleted, where the file fragment recognition issue is discussed. The known
files are divided into different fragments, and different classification
algorithms are used to solve the problems of file fragment recognition. The
issue of identifying the type of file fragment due to its importance in
cybercrime issues as well as antivirus has been highly emphasized and has been
addressed in many articles. Increasing the accuracy in this field on the types
of widely used files due to the sensitivity of the subject of recognizing the
type of file under study is the main goal of researchers in this field. Failure
to identify the correct type of file will lead to deviations of the results and
evidence from the main issue or failure to conclude. In this paper, first, the
file is divided into different fragments. Then, the file fragment features,
which are obtained from Binary Frequency Distribution, are reduced by 2 feature
reduction algorithms; Sequential Forward Selection algorithm as well as
Sequential Floating Forward Selection algorithm to delete sparse features that
result in increased accuracy and speed. Finally, the reduced features are given
to 3 Multiclass classifier algorithms, Multilayer Perceptron, Support Vector
Machines, and K-Nearest Neighbor for classification and comparison of the
results. The proposed recognition algorithm can recognize 6 types of useful
files and may distinguish a type of file fragments with higher accuracy than
the similar works done.

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