Conceptual model for crowd-sourcing digital forensic evidence

dc.contributor.authorBaror, S.O. (Stacey)
dc.contributor.authorVenter, H.S. (Hein)
dc.contributor.authorKebande, Victor Rigworo
dc.contributor.emailstacey.baror@cs.up.ac.zaen_US
dc.date.accessioned2022-08-02T11:42:10Z
dc.date.issued2022-03
dc.description.abstractCOVID-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_US
dc.description.departmentComputer Scienceen_US
dc.description.embargo2023-03-03
dc.description.librarianhj2022en_US
dc.description.urihttp://www.springer.com/series/15179en_US
dc.identifier.citationBaror, 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_88en_US
dc.identifier.isbn978-3-030-94191-8 (online)
dc.identifier.isbn978-3-030-94190-1 (print)
dc.identifier.issn2367-3370
dc.identifier.other10.1007/978-3-030-94191-8_88
dc.identifier.urihttps://repository.up.ac.za/handle/2263/86653
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG. The original publication is available at : http://www.springer.com/series/15179.en_US
dc.subjectCrowd-sourcingen_US
dc.subjectCitizen-mediaen_US
dc.subjectDigital forensicsen_US
dc.subjectDigital evidenceen_US
dc.subjectCOVID-19 pandemicen_US
dc.subjectCoronavirus disease 2019 (COVID-19)en_US
dc.titleConceptual model for crowd-sourcing digital forensic evidenceen_US
dc.typePostprint Articleen_US

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