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. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
The multiple functionalities of mobile devices have allowed them to be used for contact-tracing especially with the emergence of an infectious pandemic, for example, in a smart city. This has been experienced, for example, in COVID-19 cases where propagation of infections may not be controlled effectively. Given that data is exchanged between parties it becomes important to have a focus on how this data can be used as a contact trace mechanism. This contract trace mechanism can also provide Potential Digital Evidence (PDE) that can aid to form an objective hypothesis that could be employed during litigation in the event of a suspicious infection, or when a security incident is detected. This paper, therefore, proposes an iterative Concurrent Contact-Tracing (CCT) framework based on digital evidence from mobile devices in heterogeneous environments. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.