A Secure and Explainable AI-Based Framework for IIoT with Privacy-Prioritized Model Aggregation

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dc.contributor.advisorKarimipour, Hadis
dc.contributor.authorAflaki, Arshia
dc.contributor.committeememberTan, Peng Seng Benjamin
dc.contributor.committeememberHenry, Ryan Douglas
dc.date.accessioned2024-09-09T16:32:05Z
dc.date.available2024-09-09T16:32:05Z
dc.date.issued2024-09-09
dc.description.abstractIndustrial Control Systems (ICS) serve as indispensable components across diverse human industries, encompassing vital sectors like Oil and Gas companies, Power Grids, and Transportation. The evolution of technology has witnessed a paradigm shift in communication within ICS, transitioning from traditional systems to wireless communication for swift monitoring and control. However, this technological advancement has simultaneously exposed ICS to the online world, amplifying the susceptibility to cyber attacks and threats. Given the pivotal role of ICS in human life, establishing a robust framework to ensure the security and privacy of these systems has become an imperative. The repercussions of failing to secure ICS extend beyond data breaches, potentially endangering human lives and the environment. This thesis meticulously addresses the omnipresent threat of cyber-attacks on critical infrastructures, centering on the development of an advanced security-privacy framework tailored for ICS. The framework is designed to adeptly detect Generative Adversarial Attacks (GAAs), facilitate explainable attack attribution, and uphold privacy through the implementation of Federated Learning (FL). Innovative solutions presented in this thesis transcend traditional cybersecurity measures, providing comprehensive enhancements for critical infrastructures that include power grids and industrial systems. Beyond merely fortifying model robustness against adversarial attacks, the proposed frameworks are characterized by a steadfast commitment to prioritizing privacy. Moreover, it furnishes effective tools for attack attribution and anomaly prediction within dynamic and complex environments.
dc.identifier.citationAflaki, A. (2024). A secure and explainable AI-based framework for IIoT with privacy-prioritized model aggregation (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.urihttps://hdl.handle.net/1880/119656
dc.language.isoen
dc.publisher.facultyGraduate Studies
dc.publisher.institutionUniversity of Calgary
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectCybersecurity
dc.subjectAI
dc.subjectFederate Lerning
dc.subjectGAN
dc.subjectReinforcement Learning
dc.subject.classificationEngineering--Electronics and Electrical
dc.subject.classificationArtificial Intelligence
dc.titleA Secure and Explainable AI-Based Framework for IIoT with Privacy-Prioritized Model Aggregation
dc.typemaster thesis
thesis.degree.disciplineEngineering – Electrical & Computer
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.thesis.accesssetbystudentI do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.
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