Digital Forensic Automation Model For Online Social Networks
Presently, law enforcement agencies and legal practitioners frequently utilize social networks to quickly access the information related to the participants of any illicit incident. However, the forensic process is technically intricate due to heterogeneous and unstructured online social networks an...
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my-usm-ep.559172022-12-06T07:41:18Z Digital Forensic Automation Model For Online Social Networks 2019-09 Arshad, Humaira QA75.5-76.95 Electronic computers. Computer science Presently, law enforcement agencies and legal practitioners frequently utilize social networks to quickly access the information related to the participants of any illicit incident. However, the forensic process is technically intricate due to heterogeneous and unstructured online social networks and legally challenging. Hence, creating intellectual challenges and enormous workloads for the investigators. Therefore, it is critical to developing automated and reliable solutions to assist investigators. Though automation is not an entirely technical issue in digital forensics. Legal requirements always demand an explainable theory for the conclusions generated by automated methods. This work introduces an automation model; that addresses the automation issues from collection to evidence analysis in online social network forensics. This study first describes a formal knowledge model to explain the forensic process for the social network. This knowledge model is formulated to explain the results obtained by an automated analysis. Second, it explained a forensic investigation model that specifically addresses the issue of automated investigations on online social networks. This model suggested an investigation process to carry out a semi-automated forensic investigation on online social networks. The third component of this approach is a hybrid ontology model that involves multiple ontologies to manage the unstructured data into an organized collection. Finally, this work proposed a set of analysis operators that are on domain correlations. These operators can be embedded in software tools. 2019-09 Thesis http://eprints.usm.my/55917/ http://eprints.usm.my/55917/1/Thesis%20final%20hard%20copy%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer |
| institution |
Universiti Sains Malaysia |
| collection |
USM Institutional Repository |
| language |
English |
| topic |
QA75.5-76.95 Electronic computers Computer science |
| spellingShingle |
QA75.5-76.95 Electronic computers Computer science Arshad, Humaira Digital Forensic Automation Model For Online Social Networks |
| description |
Presently, law enforcement agencies and legal practitioners frequently utilize social networks to quickly access the information related to the participants of any illicit incident. However, the forensic process is technically intricate due to heterogeneous and unstructured online social networks and legally challenging. Hence, creating intellectual challenges and enormous workloads for the investigators. Therefore, it is critical to developing automated and reliable solutions to assist investigators. Though automation is not an entirely technical issue in digital forensics. Legal requirements always demand an explainable theory for the conclusions generated by automated methods. This work introduces an automation model; that addresses the automation issues from collection to evidence analysis in online social network forensics. This study first describes a formal knowledge model to explain the forensic process for the social network. This knowledge model is formulated to explain the results obtained by an automated analysis. Second, it explained a forensic investigation model that specifically addresses the issue of automated investigations on online social networks. This model suggested an investigation process to carry out a semi-automated forensic investigation on online social networks. The third component of this approach is a hybrid ontology model that involves multiple ontologies to manage the unstructured data into an organized collection. Finally, this work proposed a set of analysis operators that are on domain correlations. These operators can be embedded in software tools. |
| format |
Thesis |
| qualification_name |
Doctor of Philosophy (PhD.) |
| qualification_level |
Doctorate |
| author |
Arshad, Humaira |
| author_facet |
Arshad, Humaira |
| author_sort |
Arshad, Humaira |
| title |
Digital Forensic Automation Model For Online Social Networks |
| title_short |
Digital Forensic Automation Model For Online Social Networks |
| title_full |
Digital Forensic Automation Model For Online Social Networks |
| title_fullStr |
Digital Forensic Automation Model For Online Social Networks |
| title_full_unstemmed |
Digital Forensic Automation Model For Online Social Networks |
| title_sort |
digital forensic automation model for online social networks |
| granting_institution |
Universiti Sains Malaysia |
| granting_department |
Pusat Pengajian Sains Komputer |
| publishDate |
2019 |
| url |
http://eprints.usm.my/55917/1/Thesis%20final%20hard%20copy%20cut.pdf |
| _version_ |
1776101121682571264 |