A multilayered semantic framework for integrated forensic acquisition on social media. (June 2019)

• Record Type: Journal Article
• Authors: Arshad, Humaira; Jantan, Aman; Hoon, Gan Keng; Butt, Anila Sahar
• Is Part Of: Digital investigation. Volume 29(2019)
• Page Start: 147
• Page End: 158
• Publication Date: 2019-06
• Subjects: Online social network forensics -- Hybrid Ontology model -- Social network analysis -- Social network forensic automation; Forensic sciences -- Data processing -- Periodicals; Criminal investigation -- Data processing -- Periodicals; 363.250285
• DOI: 10.1016/j.diin.2019.04.002
• Languages: English
• ISSNs: 1742-2876

Abstract

Abstract: In recent years, examination of the social media networks has become an integral part of investigations. 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 needs collection and analysis of the information which is immense, heterogeneous, and spread across multiple social networks. This process is technically intricate due to heterogeneous and unstructured online social networks (OSNs). Hence, creating cognitive challenges and massive workloads for the investigators. Therefore, it is imperative to develop automated and reliable solutions to assist investigators. Capturing the forensic information in the structured form is crucial for automation, sharing, and interoperability. This paper introduces the design of a multi-layer framework; from collection to evidence analysis. The central component of this framework is a hybrid ontology approach that involves multiple ontologies to manage the unstructured data and integrate various social media data collections. This approach aims to find the evidence by automated methods that are trustworthy and therefore admissible in a court of law.