dc.contributor.advisor | Vassiliou, Vasos | en |
dc.contributor.author | Bin Masood, Abdullah M. | en |
dc.coverage.spatial | Cyprus | en |
dc.creator | Bin Masood, Abdullah M. | en |
dc.date.accessioned | 2024-05-24T06:13:03Z | |
dc.date.available | 2024-05-24T06:13:03Z | |
dc.date.issued | 2024 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/66209 | |
dc.description | Includes bibliographical references. | en |
dc.description | Number of sources in the bibliography: 214. | en |
dc.description | Thesis (Ph. D.) -- University of Cyprus, Faculty of Pure and Applied Sciences, Department of Computer Science, 2024. | en |
dc.description | The University of Cyprus Library holds the printed form of the thesis. | en |
dc.description.abstract | This thesis presents two novel frameworks, the Blockchain-Based Data-Driven Fault-Tolerant Control (BB-DD-FTC) and Blockchain-Driven Deep Reinforcement Learning (BlockDRL), designed to enhance cybersecurity and optimize resource management within Industry 4.0-enabled smart factories. The BB-DD-FTC framework leverages a blockchain-integrated DD-FTC to detect and mitigate cyber threats effectively, enhancing the robustness of IIoT systems. Simultaneously, the BlockDRL framework, utilizing DRL, innovatively addresses the challenges of computational and data storage efficiency, facilitating autonomous, optimal decision-making without reliance on third-party verification. These frameworks are rigorously validated through simulation experiments, demonstrating their efficacy in enhancing operational resilience and efficiency in smart manufacturing environments under various cyber-physical threat scenarios. | en |
dc.format.extent | | |
dc.language.iso | eng | en |
dc.publisher | Πανεπιστήμιο Κύπρου, Σχολή Θετικών και Εφαρμοσμένων Επιστημών / University of Cyprus, Faculty of Pure and Applied Sciences | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.subject.lcsh | | en |
dc.title | A Framework for Blockchain-Based Data-Driven Fault Tolerant Control in Industrial Internet of Things Enabled Smart Factories | en |
dc.type | info:eu-repo/semantics/doctoralThesis | en |
dc.contributor.committeemember | Athanasopoulos, Elias | en |
dc.contributor.committeemember | Kolios, Panayiotis | en |
dc.contributor.committeemember | Nikoletseas, Sotiris | en |
dc.contributor.committeemember | Pezaros, Demetris | en |
dc.contributor.committeemember | Vassiliades, Vassilis | en |
dc.contributor.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.subject.uncontrolledterm | BLOCKCHAIN | en |
dc.subject.uncontrolledterm | BIG DATA ANALYTICS | en |
dc.subject.uncontrolledterm | FAULT-TOLERANT CONTROL | en |
dc.subject.uncontrolledterm | INDUSTRIAL CONTROL SYSTEMS | en |
dc.subject.uncontrolledterm | SMART FACTORY | en |
dc.subject.uncontrolledterm | INDUSTRY 4.0 | en |
dc.subject.uncontrolledterm | TENNESSEE EASTMAN PROCESS | en |
dc.subject.uncontrolledterm | COMPUTATION OFFLOADING | en |
dc.subject.uncontrolledterm | DEEP REINFORCEMENT LEARNING | en |
dc.identifier.lc | | en |
dc.author.faculty | Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Doctoral Thesis | en |
dc.rights.embargodate | 2025-05-23 | |
dc.contributor.orcid | Bin Masood, Abdullah M. [0000-0003-4474-0011] | |
dc.contributor.orcid | Vassiliou, Vasos [0000-0001-8647-0860] | |
dc.contributor.orcid | Athanasopoulos, Elias [0000-0002-8759-3261] | |
dc.contributor.orcid | Kolios, Panayiotis [0000-0003-3981-993X] | |
dc.contributor.orcid | Nikoletseas, Sotiris [0000-0003-3765-5636] | |
dc.contributor.orcid | Pezaros, Demetris [0000-0003-0939-378X] | |
dc.contributor.orcid | Vassiliades, Vassilis [0000-0002-1336-5629] | |
dc.gnosis.orcid | 0000-0003-4474-0011 | |
dc.gnosis.orcid | 0000-0001-8647-0860 | |
dc.gnosis.orcid | 0000-0002-8759-3261 | |
dc.gnosis.orcid | 0000-0003-3981-993X | |
dc.gnosis.orcid | 0000-0003-3765-5636 | |
dc.gnosis.orcid | 0000-0003-0939-378X | |
dc.gnosis.orcid | 0000-0002-1336-5629 | |