Conceptual model for crowd-sourcing digital forensic evidence

Abstract

COVID-19 scourge has made it challenging to combat digital crimes due to the complexity of attributing potential security incidents to perpetrators. Existing literature does not accurately pinpoint relevant models/frameworks that can be leveraged for crowd-sourcing digital forensic evidence. This paper suggests using feature engineering approaches for crowd-sourcing digital evidence to profile potential security incidents, for example, in a COVID-19 scenario. The authors have proposed a conceptual Crowd-sourcing (CRWD) model with three main components: Forensic data collection, feature engineering and the application of machine learning approaches, and also assessment with standardized reporting. This contribution is significantly poised to solve future investigative capabilities for forensic practitioners and computer security researchers.