Holistic Analytics of Digital Artifacts

Abstract

Investigation of every crime scene with digital evidence is predominantly required in identifying almost all atomic files behind the scenes that have been intentionally scrubbed out. Apart from the data generated across digital devices and the use of diverse technology that slows down the traditional digital forensic investigation strategies. Dynamically scrutinizing the concealed or sparse metadata matches from the less frequent archives of evidence spread across heterogeneous sources and finding their association with other artifacts across the collection is still a horrendous task for the investigators. The effort of this article via unique pockets (UP), unique groups (UG), and unique association (UA) model is to address the exclusive challenges mixed up in identifying incoherent associations that are buried well within the meager metadata field-value pairs. Both the existing similarity models and proposed unique mapping models are verified by the unique metadata association model.