Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LISIC
Durée : 1 year
Contact : esteban.bautista@univ-littoral.fr
Date limite de publication : 2026-06-15

Contexte :
The Industrial Internet of Things (IIoT) is a rapidly evolving paradigm in which industrial sensors, machines, and other instruments are connected to the internet, enabling device-to-server and device-to-device communications for real-time data exchange. IIoT systems call for the detection of abnormal events in: (i) device communication logs and (ii) device measurements. For the former, devices are now exposed to attacks or intrusions, thus making it necessary to search for signs of these events in the communication logs. For the latter, any equipment or operational issues will lead to changes in IIoT measurements and potentially critical production shutdowns, making it vital to promptly detect these events to trigger corrective actions.

A natural model for IIoT data are attributed weighted temporal graphs: nodes represent devices; node features represent their measurements; and time-varying weighted edges capture various types of information, such as their communications, their statistical dependencies, flows through them, etc. In the initial phase of this project, we have already developed powerful temporal graph-based auto-encoder models capable of detecting both temporal and structural anomalies in IIoT. Yet, while our models allow us to detect the presence of an anomaly, they are still unable to localize the anomaly.

The main goal of this postdoctoral project is to fundamentally extend our temporal graph neural networks beyond anomaly detection to perform anomaly localization.

Sujet :
The recruited postdoc will have the two main goals.

1. Extension of Auto-Encoders for Anomaly Localization. Our models currently receive a temporal graph as input and produce an anomaly score that is proportional to the scope of its abnormal events. It is however impractical to test all possible subsets of a temporal graph to search for the one that maximizes anomaly score. Instead, our aim is to prune-down the search space by properly structuring the auto-encoder’s latent representation, so that sub-graphs map to sub-spaces of its containing graph.

2. Application to IIoT logs and measurements. We aim to evaluate the methodological developments above in real-world IIoT datasets that contain various types of attacks (structural anomalies) and measurement faults (feature anomalies). We aim to explore the advantages of our
approach in real-world, potentially on-line, scenarios, such as machine health monitoring, transportation network monitoring, or other use cases that may arise from collaborations with local companies.

Profil du candidat :
We look for highly motivated candidates with relevant experience in anomaly detection, graph machine learning, and/or deep learning. Experience in Python programming, cybersecurity and/or streaming algorithms is a plus. Ideal candidates will have a publication record in selective AI conferences.

Interested candidates are invited to send a cover letter, a detailed CV (with a publication list and the contact details of two references), and their PhD manuscript or a recent paper to:

– Esteban Bautista: esteban.bautista@univ-littoral.fr
– Claire Guilloteau: claire.guilloteau@univ-littoral.fr

Applications will be reviewed on a rolling basis until the position is filled.

Formation et compétences requises :
PhD in computer science

Adresse d’emploi :
LISIC Laboratory – Université du Littoral Côte d’Opale – Saint Omer, France

Document attaché : 202605140811_ADIIOT_Postdoc_offer.pdf