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

Area of Science:

• Digital Forensics
• Social Media Analysis
• Legal Technology

Background:

• Law enforcement and legal professionals increasingly use social media for investigations.
• Challenges include heterogeneous data, unstructured formats, and privacy laws, leading to high investigator workloads.
• Existing tools struggle with the complexity of online social network data for legal purposes.

Purpose of the Study:

• To develop and present a multi-layer automation approach for online social network forensics.
• To address technical and legal intricacies in collecting and analyzing social media data for legal use.
• To provide tools that assist investigators in decision-making and drawing conclusions.

Main Methods:

• A multi-layer automation framework for digital forensics on social networks.
• Development of analysis operators based on domain correlations.
• Implementation of operators using Twitter ontology.
• Testing the approach through a case study.

Main Results:

• A proof-of-concept for automated forensic analysis on online social networks.
• Demonstration of a system that handles data collection to evidence analysis.
• Validation of analysis operators for drawing realistic conclusions.

Conclusions:

• Automating digital forensics on social media requires addressing both technical and legal/privacy challenges.
• The proposed multi-layer automation approach offers a viable solution for streamlining investigations.
• Analysis operators can enhance the efficiency and accuracy of forensic investigations on social networks.