PhD Position F/M Anomaly and attack detection in the IoT paradigm

The  Inria University of Lille centre, created in 2008, employs 360 people  including 305 scientists in 15 research teams. Recognised for its strong  involvement in the socio-economic development of the Hauts-De-France  region, the Inria University of Lille centre pursues a close  relationship with large companies and SMEs. By promoting synergies  between researchers and industrialists, Inria participates in the  transfer of skills and expertise in digital technologies and provides  access to the best European and international research for the benefit  of innovation and companies, particularly in the region.For more  than 10 years, the Inria University of Lille centre has been located at  the heart of Lille's university and scientific ecosystem, as well as at  the heart of Frenchtech, with a technology showroom based on Avenue de  Bretagne in Lille, on the EuraTechnologies site of economic excellence  dedicated to information and communication technologies (ICT)

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.

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

1st and 2nd year : 2100 € (gross monthly salarye)

3rd year : 2190 € (gross monthly salary)