Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

File Fragment Recognition Based on Content and Statistical Features

Version 1 : Received: 24 January 2020 / Approved: 26 January 2020 / Online: 26 January 2020 (04:04:53 CET)

How to cite: Masoumi, M.; Keshavarz, A. File Fragment Recognition Based on Content and Statistical Features. Preprints 2020, 2020010305 (doi: 10.20944/preprints202001.0305.v1). Masoumi, M.; Keshavarz, A. File Fragment Recognition Based on Content and Statistical Features. Preprints 2020, 2020010305 (doi: 10.20944/preprints202001.0305.v1).

Abstract

Nowadays, 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 to solve the problems of file fragment recognition. A confusion matrix measures the accuracy of type recognition. In the present study, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution (BFD), are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm (SFS) as well as Sequential Floating Forward Selection algorithm (SFFS) to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 classifier algorithms, Multilayer Perceptron (MLP), Support Vector Machines (SVM), and K-Nearest Neighbor (KNN) for classification and comparison of the results. In this paper, we proposed the algorithm of file type recognition that can recognize 6 types of useful files ( pdf, txt, jpg, doc, html, exe), which may distinguish a type of file fragments with higher accuracy than the similar works done.

Subject Areas

classification algorithm; feature reduction; file fragments; file fragment recognition; SFS; SFFS

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