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Akinbi, A, MacDermott, Á and Ismael, AM (2022) A systematic literature review of blockchain-based Internet of Things (IoT) forensic investigation process models. Forensic Science International: Digital Investigation, 42. pp. 1-11. ISSN 2666-2817
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Abstract
Digital forensic examiners and stakeholders face increasing challenges during the investigation of Internet of Things (IoT) environments due to the heterogeneous nature of the IoT infrastructure. These challenges include guaranteeing the integrity of forensic evidence collected and stored during the investigation process. Similarly, they also encounter challenges in ensuring the transparency of the investigation process which includes the chain-of-custody and evidence chain. In recent years, some blockchain-based secure evidence models have been proposed especially for IoT forensic investigations. These proof-of-concept models apply the inherent properties of blockchain to secure the evidence chain of custody, maintain privacy, integrity, provenance, traceability, and verification of evidence collected and stored during the investigation process. Although there have been few prototypes to demonstrate the practical implementation of some of these proposed models, there is a lack of descriptive review of these blockchain-based IoT forensic models.
In this paper, we report a comprehensive Systematic Literature Review (SLR) of the latest blockchain-based IoT forensic investigation process models. Particularly, we systematically review how blockchain is being used to securely improve the forensic investigation process and discuss the efficiency of these proposed models. Finally, the paper highlights challenges, open issues, and future research directions of blockchain technology in the field of IoT forensic investigations.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | Elsevier BV |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 03 Oct 2022 14:13 |
| Last Modified: | 03 Oct 2022 14:15 |
| DOI or ID number: | 10.1016/j.fsidi.2022.301470 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/17715 |
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