期刊
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
卷 120, 期 -, 页码 13-25出版社
ELSEVIER
DOI: 10.1016/j.future.2021.02.016
关键词
IoT; Forensics; Digital; Blockchain; Custody; Security; Cloud
资金
- EU [H2020-SU-SEC-2018-832735]
The proposed IoF framework for IoT forensics utilizes blockchain technology to address cross-border digital forensics, providing distributed computing and transparency to overcome cloud forensic challenges and cross-border legalization barriers.
Digital forensic in Internet-of-Thing (IoT) paradigm is critical due to its heterogeneity and lack of transparency of evidence processing. Moreover, cross-border legalization makes a hindrance in such process pertaining to the cloud forensic issues. This urges a forensic framework for IoT which provides distributed computing, decentralization, and transparency of forensic investigation of digital evidences in cross-border perspectives. To this end, we propose a framework for IoT forensics that addresses the above mentioned issues. The proposed solution called Internet-of-Forensics (IoF) considers a blockchain tailored IoT framework for digital forensics. It provides a transparent view of the investigation process that involves all the stakeholders (e.g., heterogeneous devices, and cloud service providers) in a single framework. It uses blockchain-based case chain to deal with the investigation process including chain of-custody and evidence chain. Consensus is used for consortium to solve the problems of cross-border legalization. This is also beneficial for a transparent and ease of forensic reference. The programmable lattice-based cryptographic primitives produce reduced complexities. It shows benefits for power aware devices and puts an add-on to the novelty of the presented idea. IoF is generic; hence, it can be used by autonomous security operation centers, cyber-forensic investigators and manually initiated evidences under chain-of-custody for man-made crimes. Security services are assured as required by the framework. IoF is experimented and compared with the other state-of-the-art frameworks. The outcomes and analysis prove the efficiency of IoF concerning complexity, time consumption, memory and CPU utilization, gas consumption, and energy analysis. (C) 2021 Elsevier B.V. All rights reserved.
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