Khanji, S. orcid.org/0000-0002-3514-6398, Alfandi, O. orcid.org/0000-0002-9581-401X, Ahmad, L. et al. (2 more authors) (2022) A systematic analysis on the readiness of blockchain integration in IoT forensics. Forensic Science International: Digital Investigation, 42-43. 301472. ISSN 2666-2817
Abstract
Internet of Things (IoT) devices are massively utilized in our daily lives which is exposing them to a wide range of attacks. The heterogeneity of evidence produced by IoT devices is complicating the process of evidence collection and processing. Consequently, it is imperative to maintain admissible evidence collection, preservation, and analysis to be presented in a court of law. The currently used digital forensic tools and methodologies are lagging behind the IoT's heterogeneity and distributive nature. The decentralized, distributed, and transparent nature of Blockchain has encouraged lots of research on utilizing Blockchain to store, process, and investigate digital evidence in IoT forensics across various jurisdictions. Therefore, this research work analyzes proposed frameworks in the literature to review their deployment of Blockchain technology to resolve the various presented challenges in IoT Forensics. It presents a systematic review to investigate the readiness of blockchain integration in IoT forensics. Many factors have been addressed to consider when integrating the Blockchain technology into IoT forensics such as data integrity, distributed storage, authentication, transparency, and security where the literature provides an adequate proof on the crucial need to consider them as essential IoT forensic readiness factors. The research findings highlight challenges and open research opportunities of blockchain utilization to facilitate sound and efficient IoT forensics.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier Ltd. |
Keywords: | Digital forensics; IoT; Blockchain; Chain of custody; Data integrity; Distributed storage; Transparency |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Mar 2025 10:00 |
Last Modified: | 26 Mar 2025 10:00 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.fsidi.2022.301472 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224851 |