Paper Info
Title
Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events
Abstract
Case-hindering, multi-year digital forensic evidence backlogs have become commonplace in law enforcement agencies throughout the world. This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case. Leveraging previously processed digital forensic cases and their component artefact relevancy classifications can facilitate an opportunity for training automated artificial intelligence based evidence processing systems. These can significantly aid investigators in the discovery and prioritisation of evidence. This paper presents one approach for file artefact relevancy determination building on the growing trend towards a centralised, Digital Forensics as a Service (DFaaS) paradigm. This approach enables the use of previously encountered pertinent files to classify newly discovered files in an investigation. Trained models can aid in the detection of these files during the acquisition stage, i.e., during their upload to a DFaaS system. The technique generates a relevancy score for file similarity using each artefact’s filesystem metadata and associated timeline events. The approach presented is validated against three experimental usage scenarios.
Year
DOI
Venue
2020
10.1109/CyberSecurity49315.2020.9138874
2020 International Conference on Cyber Security and Protection of Digital Services (Cyber Security)
Keywords
DocType
ISSN
Automated Artefact Analysis,Evidence Prioritisation,Event-based Evidence Analysis
Conference
The 6th IEEE International Conference on Cyber Security and Protection of Digital Services (Cyber Security), Dublin, Ireland, June 2020
ISBN
Citations 
PageRank 
978-1-7281-6428-1
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Xiao-Yu Du1504.66
Quan Le200.34
Mark Scanlon32310.74