Doctorant F/H Detection d'attaques et d'anomalies dans l'Internet des Objets

Le centre Inria Université de  Lille, créé en 2008, emploie 360 personnes dont 305 scientifiques  répartis dans 15 équipes de recherche. Reconnu pour sa forte implication  dans le développement socio-économique de la région Hauts-De-France, le  centre Inria de l'Université de Lille poursuit une relation étroite  avec les grandes entreprises et les PME. En favorisant les synergies  entre chercheurs et industriels, Inria participe au transfert de  compétences et d'expertises dans le domaine des technologies numériques  et donne accès au meilleur de la recherche européenne et internationale  au profit de l'innovation et des entreprises, notamment dans la région.Depuis  plus de 10 ans, le centre Inria de l'Université de Lille est situé au  cœur de l'écosystème universitaire et scientifique de Lille, ainsi qu'au  cœur de la Frenchtech, avec un showroom technologique basé avenue de  Bretagne à Lille, sur le site d'excellence économique d'EuraTechnologies  dédié aux technologies de l'information et de la communication (TIC).

The PhD student will be co-supervised by Valeria Loscri (FUN Team) and Kevin Jiokeng (École Polytechnique).

The Inria FUN research group investigates solutions to enhance programmability, adaptability and reachability of FUN (Future Ubiquitous Networks) composed of RFID, wireless sensor and robot networks. Limited resources, and high mobility evolving in distrusted environments characterize the objects that compose FUN. They communicate in a wireless way. To be operational and efficient, such networks have to follow some self-organizing rules. Indeed, components of FUN have to be able in a distributed and energy-efficient way to discover the network, self-deploy, communicate, self- structure in spite of their hardware constraints while adapting the environment in which adapting the environment in which they evolve. For additional information on the FUN research group, please see http://team.inria.fr/fun/

The ubiquitous deployment of Internet of Things (IoT) devices, make this technology a key actor of our daily activities, by enabling advanced services and applications that could not be imagined few years ago. This PhD wiil be in the context of a National project, NEMIoT (NEtwork Methods for IoT).  This project aims at developing new methods to increase trust in IoT devices before and after their deployment on existing network infrastructures. Although many efforts have been made in securing IoT devices with regards to hardware, software, trustworthiness, data leaking, interoperability, or privacy, relatively little has been made on the issue of the availability of existing and expected services following the introduction of a (fleet of) new IoT device(s) (e.g., massive access problem) [1, 2]. After deployment, IoT devices may remain vulnerable and prone to abnormal behavior that needs to be quickly identified and mitigated. The characterization of the IoT devices and their traffic, to identify if it is normal/anomalous or under attack, will revolve on Machine Learning approaches [3].

The primary objective is developing original cross-layer solutions to finely and quickly detect and/or mitigate potential anomalies resulting from the introduction of IoT devices.

Expected outcomes

The specific outcomes will be new methods and tools resorting to a cross-layer approach, to finely characterize the activity of IoT nodes, to detect eventual anomaly behaviors, and ultimately to identify the root cause of these anomalies.

Main Activities

• Study of the State of Art of anomaly/attack detection in IoT systems
• Characterization of IoT devices behavior through Machine Learning approaches
• Design of new cross-layer detection approaches in IoT architectures
• Validation of the solutions via simulation and experiments

Additional activities :

• Writing reports
• Participation to the deliverables writing

References: 

[1] Emilie Bout, Valentin Bout, Alessandro Brighente, Mauro Conti, Valeria Loscri. Evaluation of Channel Hopping Strategies Against Smart Jamming Attacks. IEEE ICC 2023 - IEEE International Conference on Communications, IEEE, May 2023, Rome, Italy.

[2] E. Bout, V. Loscri and A. Gallais, "HARPAGON: An Energy Management Framework for Attacks in IoT Networks," in IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19959-19970, 15 Oct.15, 2022, doi: 10.1109/JIOT.2022.3172849.

[3] E. Bout, V. Loscri and A. Gallais, "How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey," in IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 248-279, Firstquarter 2022, doi: 10.1109/COMST.2021.3127267.

Compétences techniques et niveau requis :

Langues :

Compétences relationnelles :

Compétences additionnelles appréciées :

• Restauration subventionnée
• Transports publics remboursés partiellement
• Congés: 7 semaines de congés annuels + 10 jours de RTT (base temps plein) + possibilité d'autorisations d'absence exceptionnelle (ex : enfants malades, déménagement)
• Possibilité de télétravail et aménagement du temps de travail
• Équipements professionnels à disposition (visioconférence, prêts de matériels informatiques, etc.)
• Prestations sociales, culturelles et sportives (Association de gestion des œuvres sociales d'Inria)
• Accès à la formation professionnelle
• Sécurité sociale

Les deux premières années : 2100€ brut par mois.

La 3ème année : 2190€ brut par mois