PHD Thesis

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amachine learning -based approach for managing cyber -physical systemes
(2024) azeri nabila
The increasing prevalence of Cyber-Physical Systems (CPS) demands adaptive systems capable of responding to dynamic environmental changes. Traditionally, ensuring quality of service in CPS involves pre-planned activities at design time, posing challenges in highly dynamic contexts. This thesis investigates the integration of Machine Learning (ML) to bolster CPS adaptability, emphasizing security and privacy. The primary objective is to employ ML algorithms for real-time monitoring and dynamic adaptation of CPS behavior in response to environmental changes. Key contributions of this thesis encompass a meta-model for adaptive CPS, providing a conceptual foundation to comprehend and model interactions within CPS. In the second contribution, a ML-based architecture is proposed, enhancing CPS functionalities, including data and resource management, fault prediction, and proactive maintenance. A novel Collaborative ML-based architecture, founded on the second contribution, is introduced, highlighting scalability, security, heightened data privacy, and its suitability for resource-constrained environments. This Collaborative ML approach encompasses Federated Machine Learning (FML) and Split Machine Learning (SML). Validation is conducted through a case study on fault prediction in industrial CPS, and the experimentation results showcase promising outcomes.