School of Information Technology and Engineering

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Browsing School of Information Technology and Engineering by Author "Gouveia , Luis Borges (PhD)"

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    Provenance Blockchain with Predictive Auditing Framework for Mitigating Cloud Manufacturing Risks in Industry 4.0
    (Addis Ababa University, 2025-06) Mifta Ahmed; Gouveia , Luis Borges (PhD); Elefelious Getachew (PhD)
    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.