Cryptology ePrint Archive: Report 2022/114

Blockchain based AI-enabled Industry 4.0 CPS Protection against Advanced Persistent Threat

Ziaur Rahman and Xun Yi and Ibrahim Khalil

Abstract: Industry 4.0 is all about doing things in a concurrent, secure, and fine-grained manner. IoT 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 (APT) 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 PKI-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 AI aligned APT detection system. The unique contributions of the paper 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 boost trust among collaborators and ensure 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.

Category / Keywords: secret-key cryptography / Blockchain, Industry 4.0, Internet of Things, Edge IoT, Advanced Persistent Threat (APT), Deep Transfer Learning (DTL)

Original Publication (with minor differences): IEEE Internet of Things Journal
DOI:
10.1109/JIOT.2022.3147186

Date: received 30 Jan 2022

Contact author: zia at iut-dhaka edu

Available format(s): PDF | BibTeX Citation

Version: 20220131:075605 (All versions of this report)

Short URL: ia.cr/2022/114


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