4.6 Article

Collaborative Trust Blockchain Based Unbiased Control Transfer Mechanism for Industrial Automation

期刊

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
卷 56, 期 4, 页码 4478-4488

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIA.2019.2959550

关键词

Blockchain; Integrated circuits; Collaboration; Automation; Control systems; Cryptography; Blockchain; collaborative trust; control transfer; industrial control systems (ICS); security

资金

  1. National Natural Science Foundation of China [61431008, 61571300]

向作者/读者索取更多资源

In industrial automation, numerous devices are interconnected in smart factories for further monitor and control. Various infrastructure devices in industrial automation are usually used for control instruction distribution, data collection, and collaboration of the industrial applications. Recent security threats on industrial automation are more frequent and the industrial control systems lack trust mechanism. Blockchain has been introduced due to its decentralization and security promise, but the election results in the original designs could be biased without collaboration trust, which leads the blockchain-based industry applications invalid. In addition, in existing solutions, neither supernodes nor normal nodes in blockchain can transfer their control authorities for disaster backup. To address the aforementioned challenges, this article proposes a collaborative trust based unbiased control transfer mechanism (CTM), which realizes a dynamic assignment of industrial control. First, a collaborative trust based delegated proof of stake consensus is proposed for determining the authorities of control dynamically and unbiasedly, by designing a lightweight trust propagation protocol. Second, a CTM for checking, alarming, and restarting CTM is devised for the disaster backup. The simulation results demonstrate the CTM, which is feasible and effective for industrial automation security.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据