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Integrated examination and analysis model for improving mobile cloud forensic investigation

Advanced forensic techniques become inevitable to investigate the malicious activities in Cloud-based Mobile Applications (CMA). It is challenging to analyse the casespecific evidential artifact from the Mobile Cloud Computing (MCC) environment under forensically sound conditions. The Mobile Cloud I...

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Main Author: Alnajjar, Ibrahim Ali Mohammad
Format: Thesis
Language:English
English
Published: 2022
Subjects:
QA299.6-433 Analysis
Online Access:https://etd.uum.edu.my/10209/1/s902016_01.pdf
https://etd.uum.edu.my/10209/2/s902016_02.pdf
https://etd.uum.edu.my/10209/
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spelling my.uum.etd.102092023-01-11T00:20:46Z https://etd.uum.edu.my/10209/ Integrated examination and analysis model for improving mobile cloud forensic investigation Alnajjar, Ibrahim Ali Mohammad QA299.6-433 Analysis Advanced forensic techniques become inevitable to investigate the malicious activities in Cloud-based Mobile Applications (CMA). It is challenging to analyse the casespecific evidential artifact from the Mobile Cloud Computing (MCC) environment under forensically sound conditions. The Mobile Cloud Investigation (MCI) encounters many research issues in tracing and fine-tuning the relevant evidential artifacts from the MCC environment. This research proposes an integrated Examination and Analysis (EA) model for a generalised application architecture of CMA deployable on the public cloud to trace the case-specific evidential artifacts. The proposed model effectively validates MCI and enhances the accuracy and speed of the investigation. In this context, proposing Forensic Examination and Analysis Methodology using Data mining (FED) and Forensic Examination and analysis methodology using Data mining and Optimization (FEDO) models address these issues. The FED incorporates key sub-phases such as timeline analysis, hash filtering, data carving, and data transformation to filter out case-specific artifacts. The Long Short-Term Memory (LSTM) assisted forensic methodology decides the amount of potential information to be retained for further investigation and categorizes the forensic evidential artifacts for the relevancy of the crime event. Finally, the FED model constructs the forensic evidence taxonomy and maintains the precision and recall above 85% for effective decision-making. FEDO facilitates cloud evidence by examining the key features and indexing the evidence. The FEDO incorporates several sub-phases to precisely handle the evidence, such as evidence indexing, crossreferencing, and keyword searching. It analyses the temporal and geographic information and performs cross-referencing to fine-tune the evidence towards the casespecific evidence. FEDO models the Linearly Decreasing Weight (LDW) strategy based Particle Swarm Optimization (PSO) algorithm on the case-specific evidence to improve the searching capability of the investigation across the massive MCC environment. FEDO delivers the evidence tracing rate at 90%, and thus the integrated EA ensures improved MCI performance. 2022 Thesis NonPeerReviewed text en https://etd.uum.edu.my/10209/1/s902016_01.pdf text en https://etd.uum.edu.my/10209/2/s902016_02.pdf Alnajjar, Ibrahim Ali Mohammad (2022) Integrated examination and analysis model for improving mobile cloud forensic investigation. Doctoral thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
topic QA299.6-433 Analysis
spellingShingle QA299.6-433 Analysis
Alnajjar, Ibrahim Ali Mohammad
Integrated examination and analysis model for improving mobile cloud forensic investigation
description Advanced forensic techniques become inevitable to investigate the malicious activities in Cloud-based Mobile Applications (CMA). It is challenging to analyse the casespecific evidential artifact from the Mobile Cloud Computing (MCC) environment under forensically sound conditions. The Mobile Cloud Investigation (MCI) encounters many research issues in tracing and fine-tuning the relevant evidential artifacts from the MCC environment. This research proposes an integrated Examination and Analysis (EA) model for a generalised application architecture of CMA deployable on the public cloud to trace the case-specific evidential artifacts. The proposed model effectively validates MCI and enhances the accuracy and speed of the investigation. In this context, proposing Forensic Examination and Analysis Methodology using Data mining (FED) and Forensic Examination and analysis methodology using Data mining and Optimization (FEDO) models address these issues. The FED incorporates key sub-phases such as timeline analysis, hash filtering, data carving, and data transformation to filter out case-specific artifacts. The Long Short-Term Memory (LSTM) assisted forensic methodology decides the amount of potential information to be retained for further investigation and categorizes the forensic evidential artifacts for the relevancy of the crime event. Finally, the FED model constructs the forensic evidence taxonomy and maintains the precision and recall above 85% for effective decision-making. FEDO facilitates cloud evidence by examining the key features and indexing the evidence. The FEDO incorporates several sub-phases to precisely handle the evidence, such as evidence indexing, crossreferencing, and keyword searching. It analyses the temporal and geographic information and performs cross-referencing to fine-tune the evidence towards the casespecific evidence. FEDO models the Linearly Decreasing Weight (LDW) strategy based Particle Swarm Optimization (PSO) algorithm on the case-specific evidence to improve the searching capability of the investigation across the massive MCC environment. FEDO delivers the evidence tracing rate at 90%, and thus the integrated EA ensures improved MCI performance.
format Thesis
author Alnajjar, Ibrahim Ali Mohammad
author_facet Alnajjar, Ibrahim Ali Mohammad
author_sort Alnajjar, Ibrahim Ali Mohammad
title Integrated examination and analysis model for improving mobile cloud forensic investigation
title_short Integrated examination and analysis model for improving mobile cloud forensic investigation
title_full Integrated examination and analysis model for improving mobile cloud forensic investigation
title_fullStr Integrated examination and analysis model for improving mobile cloud forensic investigation
title_full_unstemmed Integrated examination and analysis model for improving mobile cloud forensic investigation
title_sort integrated examination and analysis model for improving mobile cloud forensic investigation
publishDate 2022
url https://etd.uum.edu.my/10209/1/s902016_01.pdf
https://etd.uum.edu.my/10209/2/s902016_02.pdf
https://etd.uum.edu.my/10209/
_version_ 1755875153048240128
score 13.211869

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