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Safety/Security Modeling for Security Characterization of Industrial Control Systems

Département Systèmes (LETI)

Laboratoire Sécurité des Objets et des Systèmes Physiques

01-10-2021

SL-DRT-21-0031

Cyber security : hardware and sofware (.pdf)

Industrial systems are often used to monitor and control a physical process such as energy production and distribution, water cleaning or transport systems. They are often simply called Supervisory Control And Data Acquisition (SCADA) systems. Due to their interaction with the real world, the safety of these systems is critical and any incident can potentially harm humans and the environment. Since the Stuxnet worm in 2010, such systems increasingly face cyberattacks caused by various intruders, including terrorists or enemy governments [1]. As the frequency of such attacks is increasing, the security of SCADA systems becomes a priority for governmental agencies [2]. One of the main research axis in cybersecurity of industrial systems deals with combination of safety and security properties. Safety relates to applicative properties of the system (e.g. chemical properties for a chemical factory); while security properties take into account how an intruder can harm the system. As show in [3], combining safety and security is a challenging topic as these properties can be either dependent, strengthening, antagonist or independent. As show in [4], combining both safety and security in a common modeling is challenging as both come with sources of combinatorial explosion. Moreover, there are tools used either for security or safety analyzes but currently no tool is able to handle both aspects at the same time. In this context, we propose a Ph.D thesis revolving around modeling of industrial systems taking into account both safety properties of the physical process and security properties. Besides the definition of an accurate, yet automatically analyzable modeling framework/language, many aspects can be part of the subject. For instance, programmable automata (PLC) configuration files could be generated from this model in order to only deploy programs validated beforehand. PLC vulnerabilities could be studied (firmware reverse engineering, protocol fuzzing) in order to test the technical feasibility of found attacks. Finally, in a certification context, security analyzes on the model could include requirements from standards such as IEC 62443 [5] to help evaluation process. Références [1] J. Weiss, Protecting industrial control systems from electronic, Momentum Press, 2010. [2] ANSSI, Managing cybersecurity for ICS, ANSSI, 2012. [3] L. Piètre-Cambacédès, Des relations entre sûreté et sécurité, Paris: Télécom ParisTech, 2010. [4] M. P. a. A. K. M. Puys, Generation of applicative attacks scenarios against industrial systems, Nancy: FPS'17, 2017. [5] IEC-62443, Industrial communication networks - Network and, International Electrotechnical Commission, 2010.

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Fault injection and integrity of edge neural networks: attacks, protections, evaluation

Département Systèmes (LETI)

Laboratoire Sécurité des Objets et des Systèmes Physiques

01-02-2021

SL-DRT-21-0159

pierre-alain.moellic@cea.fr

Artificial intelligence & Data intelligence (.pdf)

One of the major trends of Artificial Intelligence is the large-scale deployment of Machine Learning systems to a large variety of embedded platforms. A lot of semi-conductor practioners propose "A.I. suitable" products, majoritarely with neural networks for inference purpose. The security of the embedded models is a major issue for the deployment of these systems. Several works raised threats such as the adversarial examples or the membership inference attacks with disastrous impact. These works consider the ML aglorithms through a pure algorithmic point of view without taking into consideration the specificities of their physical implementation. Moreover, advanced works are compulsory for physical attacks (i.e., side-channel and fault injection analysis). By considering a overall attack surface gathering the theoretical (i.e. algorithmic) and physical facets, this subject propose to analyze Fault Injection Analysis threats (FIA) targeting the integrity of the model (fooling a prediction) of embedded machine learning systems and the development of appropriate protections. Several works have studied physical attacks for embedded neural networks but with usually naive model architecture on 'simple' 8-bit microcontrolers, or FPGA or at a pure simulation level. These works do not try to link the fault models or the leakages with well-known algorithmic threats. Being based on the experience on other critical systems (e.g., cryptographic primitive), the main idea of this PhD subject will be to jointly analysis the algorithmic and physical world in order to better understand the complexity of the threats and develop efficient defense schemes. The works will answer the following scientific challenges: (1) Caracterization and exploitation of fault models: how to exploit fault injection mechanisms (laser, EM, glitching) to fool the prediction of a model with minimal perturbations. (2) Evaluation of the relevance of classical countermeasures (such as redundancy-based techniques) for this kind of systems and threats. (3) Develop new protections suitable to embedded neural networks.

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Autonomous network management solution for deterministic networks using Artificial Intelligence (AI) techniques

Département Intelligence Ambiante et Systèmes Interactifs (LIST)

Laboratoire Systèmes Communiquants

01-02-2021

SL-DRT-21-0178

siwar.benhadjsaid@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

The objective of the thesis is to explore and evaluate the gain that could bring the Artificial Intelligence (AI) techniques to the network management solutions for deterministic networks. The goal is to help deterministic networks to ensure the preservation of the quality of service (QoS) during the routing of end-to-end data flow no matter what changes are made to the network. This will allow to design an autonomous network management solution that is able to configure deterministic networks in the most appropriate way and adapt the configuration when needed (e.g. new terminal connecting to the network, unexpected high latency for certain critical flows, change of the topology caused by the reorganization / reconfiguration of components of the production chain in the factory etc.). This solution will use artificial intelligence methods to learn from experience the conditions that lead to non-compliance with application flow requirements (high latency, low bandwidth, etc.). Learning takes place to recognize, upstream, the situations that may lead to non-compliance with the constraints of application flows and also to predict the effects of changes in input data (new terminal, reorganization of the plant, etc.) on the level of QoS provided to flows in transit. Based on such knowledge, the solution will anticipate QoS degradation situations and, consequently, will decide and push the adequate network reconfiguration which will make it possible to respect the constraints associated with each application flow.

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Distributed resource allocation for meshed networks of mobile users in shared spectrum

Département Systèmes (LETI)

Laboratoire Sans fils Haut Débit

01-09-2021

SL-DRT-21-0186

mickael.maman@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

In future 5G wireless networks, it is imperative to easily deploy and manage a private network of mobile users such as a fleet of vehicles or UAVs. The objective of this thesis is to define a distributed resource allocation for mesh networks of mobile users in the shared spectrum through resource pooling (time/frequency) and efficient management of directional antenna beams. While existing studies focus on maximizing the performance of static backhaul multi-beam mesh networks, we are interested in collaborative local/distributed learning between mobile users. The first step of this thesis will be to integrate a realistic model of sub 6-GHz and/or mmW directional antennas in a network simulator. A trade off between the spatiality of the directivity, the antenna efficiency and the complexity of the algorithm will be made for point-to-point and point-to-multipoint communications. Moreover the antenna configurations will be contextualized between the communication, discovery or tracking phases. The second step of this thesis will concern the design of the distributed resource allocation protocol during different stages of the network life: deployment, self-optimization and self-healing. A trade-off will be made between the type and latency of antenna (re)configuration, the accuracy of beam alignment, the channel coherence time for mobile users (volatile connectivity) and the convergence time of the scheduling.

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Innovative flexible piezoelectric sensors for tactile and acoustic interface ? Multifunction sensors

Département Systèmes (LETI)

Laboratoire Autonomie et Intégration des Capteurs

01-09-2021

SL-DRT-21-0205

elise.saoutieff@cea.fr

Cyber physical systems - sensors and actuators (.pdf)

The aim of the PhD thesis is to implement a matrix of flexible piezoelectric nanosensors, which enable the 3D reconstruction of a force or deformation field. The nanosensors based on GaN nanowires obtained by directed growth are fabricated and assembled at CEA. The candidate will tackle experimental aspects, which include the fabrication and the assembly of sensors and sensor networks (matrix) via controlled growth and deposition processes, first-level flexible electronic layers (interconnects), system integration on an object (mechatronics) and finally signal collection and processing through a dedicated reading electronics, to be designed based on the competences present in our laboratory. In parallel, the candidate will carry out studies at the fundamental level, such as investigating the mechanical transfer between the nanowire and its environment and its effect on the generated signal under deformation, or the study of the piezoelectric / pyroelectric coupling intrinsic to GaN nanowires. For this purpose, the candidate will have access to multi-physics simulation tools. Finally, investigations on the choice of materials and the characterisation thereof (structural, mechanical, thermal, optical, electrical) will be pertinent and may pursued. More generally, this PhD thesis will also provide the opportunity to develop applicable solutions in various fields such as deformation and impacts detectors for predictive maintenance, sensitive surfaces or electronic skin.

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Wideband mmW receiver architectures based on innovative modulation schemes

Département Systèmes (LETI)

Laboratoire Architectures Intégrées Radiofréquences

01-03-2021

SL-DRT-21-0216

joseluis.gonzalezjimenez@cea.fr

Communication networks, IOT, radiofrequencies and antennas (.pdf)

Existing telecommunication and data communication networks are evolving towards extremely high capacity and data-rate connections that will require innovative transceiver architectures. For wireless data links, beyond 5G and 6G and systems will be required in the next 5 to 10 years able to provide 100Gb/s or higher data rates by efficiently using the wide spectrum available at millimeter-wave (mmW) or sub-THz frequencies. Traditional transceiver architecture that have been used in the past may result too power consuming or simply not performant enough to respond to this challenge. The LETI research institute has been conducting research during the lasts year in the field of innovative modulations schemes and transceiver architectures trying to respond to the above-mentioned high data-rate in wireless environments considering the limitation imposed by existing electronic devices required to build the transceivers. This thesis subject will explore the practical implementation of circuits based on innovative modulation schemes and architectures for high-speed, large-bandwidth, imperfection resilient mmW receivers for beyond 5G and 6G telecommunication applications and other high data-rate wireless communications applications.

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