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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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.usm.my/55917/1/Thesis%20final%20hard%20copy%20cut.pdf http://eprints.usm.my/55917/ |
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| Institution: | Universiti Sains Malaysia |
| Language: | English |
| Summary: | 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. |
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