Conceptual Model for Crowd-Sourcing Digital Forensic Evidence

Abstract

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 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.

A Conceptual Digital Forensic Investigation Model Applicable to the Drone Forensics Field

“…Essentially, both stages are used in digital forensics, and the ISO/IEC 27043:2015 standard serves as the primary context for studies on proactive forensic approaches [5][6][7][8][9][10][11][12][13][14]. Drone forensics (DRF) aims to provide the tools and techniques necessary to identify and investigate potential drone-related incidents [15][16][17]. The primary focus of DRF is to identify the drone's owner, the activities that occurred during a dronerelated incident, the nature of the data stored on the drone, and any evidence the drone may have left behind.…”