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. 2022 Feb 1;22(3):1115.
doi: 10.3390/s22031115.

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

Humaira Arshad  1 , Saima Abdullah  1 , Moatsum Alawida  2 , Abdulatif Alabdulatif  3 , Oludare Isaac Abiodun  4 , Omer Riaz  5
Affiliations

A Multi-Layer Semantic Approach for Digital Forensics Automation for Online Social Networks

Humaira Arshad et al. Sensors (Basel). .

Abstract

Currently, law enforcement and legal consultants are heavily utilizing social media platforms to easily access data associated with the preparators of illegitimate events. However, accessing this publicly available information for legal use is technically challenging and legally intricate due to heterogeneous and unstructured data and privacy laws, thus generating massive workloads of cognitively demanding cases for investigators. Therefore, it is critical to develop solutions and tools that can assist investigators in their work and decision making. Automating digital forensics is not exclusively a technical problem; the technical issues are always coupled with privacy and legal matters. Here, we introduce a multi-layer automation approach that addresses the automation issues from collection to evidence analysis in online social network forensics. Finally, we propose a set of analysis operators based on domain correlations. These operators can be embedded in software tools to help the investigators draw realistic conclusions. These operators are implemented using Twitter ontology and tested through a case study. This study describes a proof-of-concept approach for forensic automation on online social networks.

Keywords: automation tools; evidence analysis; experimental visualization; forensic applications; forensic automation; semantic data presentation; social network forensics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The multi-layered conceptual model.
Figure 2
Figure 2
The architecture of the multi-layered model.
Figure 3
Figure 3
Implementation details of the multi-layered model.
Figure 4
Figure 4
Raw data downloaded by Twitter scraper.
Figure 5
Figure 5
PHP codes for Facebook API data retrieval.
Figure 6
Figure 6
PHP codes for Twitter API data retrieval.
Figure 7
Figure 7
Interaction graph (a) from the subject to her contacts; (b) from contacts to subject.
Figure 8
Figure 8
(a) Temporal activity graph for the subject. (b) Temporal activity graph for one of the users.
Figure 9
Figure 9
Tweet cloud generated from the tweets of a cyber-bullying suspect.
Figure 10
Figure 10
(a) The objects of the subject re-shared by her contacts. (b) The objects of other users re-shared by the subject.
Figure 11
Figure 11
(a) Timeline of places visited by subjects. (b) Raw geographic data.
Figure 12
Figure 12
Links from evidence to the objects.
See this image and copyright information in PMC

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