Provenance Blockchain with Predictive Auditing Framework for Mitigating Cloud Manufacturing Risks in Industry 4.0
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Date
2025-06
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Addis Ababa University
Abstract
Cloud manufacturing is an evolving concept that enables various manufacturers to connect
and address shared demand streams regardless of their geographical location. Although
this transformation facilitates operational flexibility and resource optimization,
it concurrently introduces critical challenges related to continuous visibility, traceability,
and proactive security management within Industrial Internet of Things (IIoT)-enabled
cloud manufacturing environments. Notably, the absence of real-time insights into device
states and operational behaviors increases susceptibility to unauthorized access, latent
security breaches, and operational disruptions, whereas existing blockchMLn-based solutions
predominantly emphasize initial authentication and transactional integrity but lack
mechanisms for ongoing device verification and continuous provenance tracking. Simultaneously,
artificial intelligence (ML)-driven predictive auditing techniques have evolved in
isolation, without harnessing the immutability, accountability, and policy enforcement capabilities
afforded by blockchMLn technology. This fragmentation results in limited traceability
and weakened system integrity, particularly in dynamic IIoT ecosystems, where
timely data-driven decision making is imperative. This study MLms to address these
gaps through three primary objectives: (i) optimize blockchMLn architectures to support
continuous monitoring, traceability, and visibility in IIoT environments; (ii) develop
and integrate ML-based predictive auditing mechanisms with blockchMLn to proactively
detect and mitigate security risks in IIoT-based cloud manufacturing; and (iii) evaluate
the effectiveness of the integrated blockchMLn and predictive auditing framework in
addressing security, traceability, and real-time visibility challenges while mMLntMLning
operational continuity. Adopting a Design Science Research Methodology (DSRM), this
study develops and rigorously evaluates an integrated framework that combines dynamic
blockchMLn-based provenance logging with ML-driven anomaly detection. The experi-mental evaluation was conducted using a scenario-based experimental setup in a cloud
simulated multizone warehouse environment involving IIoT-enabled forklifts that operated
under three behavioral scenarios: fully compliant, partially compliant, and rogue.
Key evaluation metrics included validation accuracy 94%, prediction precision (up to
99.7%, F1 score 90%, traceability rate (ranging from 82% to 85%, average system latency
(3.95 seconds), transaction rejection rate (100% for rogue inputs), and operational
uptime (100% resilience with no downtime). The results substantiate the ability of the
framework to provide real-time responsiveness, robust security, and continuous traceability
while mMLntMLning operational continuity, even under adversarial or non-compliant
conditions. This study contributes to the body of knowledge by bridging the gap between
blockchMLn technology and ML in IIoT-enabled cloud-manufacturing security.
These findings have practical implications for the secure deployment of IIoT technologies
across smart manufacturing ecosystems.
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Keywords
Cloud Manufacturing, IIoT, Industry 4.0, Risks, Provenance, Predictive Auditing. Integrated Visualization