Blockchain-Based AI-Enabled Industry 4.0 CPS Protection Against Advanced Persistent Threat

【Author】 Rahman, Ziaur; Yi, Xun; Khalil, Ibrahim

【Source】IEEE INTERNET OF THINGS JOURNAL

【影响因子】10.238

【Abstract】Industry 4.0 is all about doing things in a concurrent, secure, and fine-grained manner. Internet of Things edge sensors and their associated data play a predominant role in today's industry ecosystem. Breaching data or forging source devices after injecting advanced persistent threats (APTs) damages the industry owners' money and loss of operators' lives. The existing challenges include APT injection attacks targeting vulnerable edge devices, insecure data transportation, trust inconsistencies among stakeholders, incompliant data storing mechanisms, etc. Edge servers often suffer because of their lightweight computation capacity to stamp out unauthorized data or instructions, which, in essence, makes them exposed to attackers. When attackers target edge servers while transporting data using traditional public-key infrastructure-rendered trusts, consortium blockchain (CBC) offers proven techniques to transfer and maintain those sensitive data securely. With the recent improvement of edge machine learning, edge devices can filter malicious data at their end, which largely motivates us to institute a blockchain and artificial intelligence-aligned APT detection system. The unique contributions of this article include efficient APT detection at the edge and transparent recording of the detection history in an immutable blockchain ledger. In line with that, the certificateless data transfer mechanism boosts trust among collaborators and ensures an economical and sustainable mechanism after eliminating existing certificate authority. Finally, the edge-compliant storage technique facilitates efficient predictive maintenance. The respective experimental outcomes reveal that the proposed technique outperforms the other competing systems and models.

【Keywords】Blockchains; Fourth Industrial Revolution; Image edge detection; Task analysis; Security; Industries; Transfer learning; Advanced persistent threat (APT); blockchain; deep transfer learning (DTL); edge Internet of Things (IoT); industry 4, 0; IoT

【发表时间】2023 15-Apr

【收录时间】2023-05-26

【文献类型】理论模型

【主题类别】

区块链应用-实体经济-工业互联网