PhD Position F/M Signal and attack detection infrastructure based on heterogeneous antennas in wireless networks

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 Nicolas Battaglini (Université Paris Cité)

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 main objectives of the thesis can be summarized as follows:

1) Design and development of a surveillance infrastructure based on heterogeneous nodes, including thin and flexible planar antennas made of conductive nanomaterials.

2) Characterization of an environment based on infrastructure nodes, in both passive and active scenarios.

3) Collection and processing of data/signals and development of machine learning approaches for device and communication technology identification, real-time demodulation, and tracking [alla-acsac24][bou21].

4) Ability to jam target devices while minimizing the impact on neighboring devices.

[alla-acsac24] Ildi Alla, Selma Yahia, Valeria Loscri, Hossien Eldeeb. Robust Device Authentication in Multi-Node Networks: ML-Assisted Hybrid PLA Exploiting Hardware Impairments. Annual Computer SecurityApplications Conference (ACSAC), Dec 2024, Waikiki, Hawaii, USA, United States. hal-0472749

[bou21] Emilie Bout, Valeria Loscri, Antoine Gallais. How Machine Learning changes the nature of cyber-attacks on IoT networks: A survey. Communications Surveys and Tutorials, IEEE Communications Society, Institute of Electrical and Electronics Engineers, 2021. hal-03390359

To achieve the objectives of this thesis, we plan to select a candidate with a background in signal processing. The following tasks are planned:

[M0-M6] Review of the literature on approaches for device detection, localization, and identification;

[M3-M10] Design of flexible antennas and selection of reprogrammable cards for indoor and outdoor applications. Initial tests for initial data collection in controlled environments.

[M9-M24] Design of algorithms for environmental characterization in the "passive" and "active" cases, and indoor/outdoor. Development of detection, geolocation, and environmental characterization algorithms based on machine learning approaches. Analysis of key characteristics and parameters for the construction phase of the monitoring infrastructure.

Design of algorithms for the detection and identification of devices and their occupancy in the transmission spectrum. Development of anti-jamming solutions.

[M24-M30] Experimentation and validation of detection approaches in real-world scenarios based on reprogrammable cards and flexible antennas.

Testing and validation of identification of anti-jamming devices and solutions.

[M30-36] Finalization of activities, thesis writing, and defense.

• 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

2 200 € gross monthly salary