Baror, S.O. (Stacey)Venter, H.S. (Hein)Kebande, Victor Rigworo2022-08-022022-03Baror, S.O., Venter, H.S., Kebande, V.R. (2022). Conceptual Model for Crowd-Sourcing Digital Forensic Evidence. In: Ben Ahmed, M., Boudhir, A.A., Karaș, İ.R., Jain, V., Mellouli, S. (eds) Innovations in Smart Cities Applications Volume 5. SCA 2021. Lecture Notes in Networks and Systems, vol 393.. Springer, Cham. https://doi.org/10.1007/978-3-030-94191-8_88978-3-030-94191-8 (online)978-3-030-94190-1 (print)2367-337010.1007/978-3-030-94191-8_88https://repository.up.ac.za/handle/2263/86653COVID-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.en© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG. The original publication is available at : http://www.springer.com/series/15179.Crowd-sourcingCitizen-mediaDigital forensicsDigital evidenceCOVID-19 pandemicCoronavirus disease 2019 (COVID-19)Postprint Article