Secure Cyber-Physical Systems with Machine Learning components (ML-CPS) // Secure Cyber-Physical Systems with Machine Learning components (ML-CPS)

The increasing integration of machine learning components into the control and supervision of cyber-physical systems (CPS)—which interconnect heterogeneous elements such as physical processes, digital computing units, smart sensors, and communication networks—has enabled the achievement of more complex objectives with improved performance. Beyond classical threats affecting CPS, including denial-of-service, replay, and data injection attacks, CPS integrating ML, referred to as ML-CPS, are exposed to additional system-specific vulnerabilities arising at both the training and inference stages.

The objective of the thesis is to develop effective strategies for the reliable detection of such attacks and for mitigating their impact on system performance. From a scientific perspective, the project goes beyond classical resilient and robust control frameworks, as well as traditional attack detection and isolation approaches, to specifically address the novel vulnerabilities introduced by machine learning–based systems.
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The increasing integration of machine learning components into the control and supervision of cyber-physical systems (CPS)—which interconnect heterogeneous elements such as physical processes, digital computing units, smart sensors, and communication networks—has enabled the achievement of more complex objectives with improved performance. Beyond classical threats affecting CPS, including denial-of-service, replay, and data injection attacks, CPS integrating ML, referred to as ML-CPS, are exposed to additional system-specific vulnerabilities arising at both the training and inference stages.

The objective of the thesis is to develop effective strategies for the reliable detection of such attacks and for mitigating their impact on system performance. From a scientific perspective, the project goes beyond classical resilient and robust control frameworks, as well as traditional attack detection and isolation approaches, to specifically address the novel vulnerabilities introduced by machine learning–based systems.
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Début de la thèse : 01/10/2026
WEB : http://www-verimag.imag.fr/PEOPLE/Thao.Dang/theseSecuIA.pdf

Concours allocations

Université Grenoble Alpes

217 MSTII - Mathématiques, Sciences et technologies de l'information, Informatique

Solid competences in computer science and applied mathematics; knowledge in formal methods, competences and experience in programming
Solid competences in computer science and applied mathematics; knowledge in formal methods, competences and experience in programming