Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine

File carving tools are essential element of digital forensic investigation for recovering evidence data from computer disk drives. Today, JPEG image files are popular file formats that have less structured contents which make its carving possible in the absence of any file system metadata. However,...

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Main Author:	Raad Ali, Rabei
Format:	Thesis
Language:	English English English
Published:	2019
Subjects:	QA Mathematics
Online Access:	http://eprints.uthm.edu.my/116/1/24p%20RABEI%20RAAD%20ALI.pdf http://eprints.uthm.edu.my/116/2/RABEI%20RAAD%20ALI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/116/3/RABEI%20RAAD%20ALI%20WATERMARK.pdf
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spelling	my-uthm-ep.1162021-06-22T08:06:49Z Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine 2019-07 Raad Ali, Rabei QA Mathematics File carving tools are essential element of digital forensic investigation for recovering evidence data from computer disk drives. Today, JPEG image files are popular file formats that have less structured contents which make its carving possible in the absence of any file system metadata. However, completely recovering intertwined Bifragmented JPEG images into their original form without missing any parts or data of the image is a challenging due to the intertwined case might occur with non-JPEG images such as PDF, Text, Microsoft Office or random data. In this research, a new carving framework is presented in order to address the fragmentation issues that often occur in JPEG images which is called RX_myKarve. The RX_myKarve is an extended framework from X_myKarve, which consists of the following key components: (i) an Extreme Learning Machine (ELM) neural network for clusters classification using three existing content-based features extraction (Entropy, Byte Frequency Distribution (BFD) and Rate of Change (RoC)) to improve the identification of JPEG images content and support the reassembling process; (ii) a genetic algorithm with Coherence Euclidean Distance (CED) matric and cost function to reconstruct a JPEG image from a set of deformed and fragmented clusters in the scan area. The RX_myKarve is a framework that contains both structure-based carving and content-based carving approaches. The RX_myKarve is implemented as an Automatic JPEG Carver (AJC) tool in order to test and compare its performance with the state-of-the art carvers such as RevIt, myKarve and X_myKarve. It is applied to three datasets namely DFRWS (2006 and 2007) forensic challenges datasets and a new dataset to test and evaluate the AJC tool. These datasets have complex challenges that simulate particular fragmentation cases addressed in this research. The final results show that the AJC with the aid of the RX_myKarve framework outperform the X_myKarve, myKarve and RevIt. The RX_myKarve is able to completely carve 23.8% images more than X_myKarve, 45.4% images more than myKarve and 67% images more than RevIt in which AJC tool using RX_myKarve completely solves the research problem. 2019-07 Thesis http://eprints.uthm.edu.my/116/ http://eprints.uthm.edu.my/116/1/24p%20RABEI%20RAAD%20ALI.pdf text en public http://eprints.uthm.edu.my/116/2/RABEI%20RAAD%20ALI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/116/3/RABEI%20RAAD%20ALI%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA Mathematics
spellingShingle	QA Mathematics Raad Ali, Rabei Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine
description	File carving tools are essential element of digital forensic investigation for recovering evidence data from computer disk drives. Today, JPEG image files are popular file formats that have less structured contents which make its carving possible in the absence of any file system metadata. However, completely recovering intertwined Bifragmented JPEG images into their original form without missing any parts or data of the image is a challenging due to the intertwined case might occur with non-JPEG images such as PDF, Text, Microsoft Office or random data. In this research, a new carving framework is presented in order to address the fragmentation issues that often occur in JPEG images which is called RX_myKarve. The RX_myKarve is an extended framework from X_myKarve, which consists of the following key components: (i) an Extreme Learning Machine (ELM) neural network for clusters classification using three existing content-based features extraction (Entropy, Byte Frequency Distribution (BFD) and Rate of Change (RoC)) to improve the identification of JPEG images content and support the reassembling process; (ii) a genetic algorithm with Coherence Euclidean Distance (CED) matric and cost function to reconstruct a JPEG image from a set of deformed and fragmented clusters in the scan area. The RX_myKarve is a framework that contains both structure-based carving and content-based carving approaches. The RX_myKarve is implemented as an Automatic JPEG Carver (AJC) tool in order to test and compare its performance with the state-of-the art carvers such as RevIt, myKarve and X_myKarve. It is applied to three datasets namely DFRWS (2006 and 2007) forensic challenges datasets and a new dataset to test and evaluate the AJC tool. These datasets have complex challenges that simulate particular fragmentation cases addressed in this research. The final results show that the AJC with the aid of the RX_myKarve framework outperform the X_myKarve, myKarve and RevIt. The RX_myKarve is able to completely carve 23.8% images more than X_myKarve, 45.4% images more than myKarve and 67% images more than RevIt in which AJC tool using RX_myKarve completely solves the research problem.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Raad Ali, Rabei
author_facet	Raad Ali, Rabei
author_sort	Raad Ali, Rabei
title	Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine
title_short	Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine
title_full	Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine
title_fullStr	Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine
title_full_unstemmed	Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine
title_sort	reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2019
url	http://eprints.uthm.edu.my/116/1/24p%20RABEI%20RAAD%20ALI.pdf http://eprints.uthm.edu.my/116/2/RABEI%20RAAD%20ALI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/116/3/RABEI%20RAAD%20ALI%20WATERMARK.pdf
_version_	1747830532965662720

Reassembly and clustering bifragmented intertwined jpeg images using genetic algorithm and extreme learning machine

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