Requirements
- Machine learning
- Deep learning
- Graph neural networks
- Federated learning
- Network security
- Industrial IoT and cyber-physical systems
- Data analysis
- Python (PyTorch, TensorFlow, PyG)
- Experimental evaluation
- Scientific writing
Research Work
Scientific context: The rapid deployment of IIoT devices across manufacturing, smart energy, and logistics sectors has profoundly transformed industrial architectures, giving rise to a new generation of cyber-physical systems (CPS) whose security is critical to operational continuity. Subject: This thesis proposes the design, implementation, and evaluation of a federated GNN-based intrusion detection framework for Industrial IoT networks (IIoT). Its originality lies in the combination of three complementary dimensions: (i) federated learning specifically adapted to the constraints and heterogeneity of industrial environments, (ii) graph neural network architectures tailored to the topology of IIoT communication systems, and (iii) the exploitation of complex network properties to optimize both the learning model and the federation process.
Prior works in the laboratory
The contributions of this thesis project build directly on an established and coherent body of research conducted by the CESI LINEACT team, in collaboration with the Lebanese University, through the jointly supervised theses of Mortada Termos and Fouad Al Tfaily.
Additional Information
- Starting date: 01/09/2026
- Hosted at CESI LINEACT research department in France
- Access to 'Industry of the Future' demonstrator
- IoT platform in Canada for industrial smart buildings
- Joint supervision between France and Canada
