Abstract
In digital forensics, knowledge graphs have demonstrated significant potential via software agent automation and knowledge discovery using encoded expert knowledge in, for example, the form of Semantic Web rules. These advancements have been limited in terms of efficiently encoding extracted digital artifacts into graph representation, the associated overhead of implementing frameworks to handle digital forensic evidence, and the lack of sharing of such code that is often a preamble to other research. This paper introduces a digital forensic framework, Forensic Extraction and Representation (FEAR), that enables a simplified process of accessing and encoding extracted digital forensic artifacts in semantic knowledge graphs. The adoption of such a framework can facilitate the sharing of expert knowledge and reduce the burden of development for researchers exploring the application of knowledge graphs, software agents, and automated reasoning in the field of digital forensics, while accelerating the adoption of emerging research by practitioners.
Keywords
digital forensic artifacts, semantic knowledge graph
Document Type
Conference Proceeding
Date of Publication
2025
Publication Title
Digital Forensics Doctoral Symposium
Publisher
ACM
School
School of Science
RAS ID
84464
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Korol, A., & Sikos, L. F. (2025, April). FEAR: A novel framework for representing digital forensic artifacts in knowledge graphs. In Proceedings of the Digital Forensics Doctoral Symposium (pp. 1-8). https://doi.org/10.1145/3712716.3712726