Doctorant

Offer Description

TIMA laboratory is a public research laboratory under the joint supervision of CNRS, Grenoble INP, and Université Grenoble Alpes (UGA). It is located in Grenoble, on the Viallet campus of Grenoble INP-UGA (46 avenue Félix Viallet).

TIMA is a multicultural team of approximately 150 people, including researchers from around the world (permanent researchers, faculty members, PhD students, postdoctoral researchers, fixed-term research engineers, scientific collaborators, visiting researchers, and interns).

The laboratory's research activities cover the specification, design, verification, and testing of integrated systems, as well as computer-aided design (CAD) tools and design support methodologies.

A significant proportion of this research is conducted through collaborative projects involving industrial and academic partners, funded by regional, national, and European research programmes. The laboratory therefore maintains strong ties with both industry and academia, requiring a high level of professional involvement in terms of both logistics and stakeholder relations.

The black-box or grey-box security assessment of embedded systems against side-channel attacks often requires a preliminary reverse-engineering phase, which can be particularly time-consuming, especially when dealing with complex Systems-on-Chip (SoCs) such as those found in smartphones or automotive systems. This phase may involve, for example, detecting a cryptographic primitive in side-channel measurements in preparation for a future observation-based attack, or identifying a target routine for a fault-injection attack.

The objective of this PhD project is to develop an unprofiled methodology and tools to automate this detection phase, while enabling the use of an attacker's prior knowledge.

Unprofiled side-channel reverse engineering is closely related to well-established time-series analysis problems, such as motif discovery, anomaly detection, and semantic segmentation. One of the objectives of this PhD project will therefore be to address these challenges while accounting for the specific characteristics of side-channel attacks on SoCs, such as measurement noise and jitter, as well as an attacker's prior knowledge of the executed code and the data being processed.

In this context, investigating a methodology that combines machine learning, probabilistic models, and exact time-series metrics will be a cornerstone of this PhD project. The research problems addressed by this thesis encompass a wide range of scenarios. Beyond preparing for conventional physical attacks, notable examples include bootloader reverse engineering and the discovery of neural network architectures.

Where to apply

https://emploi.cnrs.fr/Offres/Doctorant/UMR5159-VIVGIO-008/Default.aspx

Requirements

• Research Field: Engineering; Education Level: Master Degree or equivalent
• Research Field: Computer science; Education Level: Master Degree or equivalent
• Research Field: Mathematics; Education Level: Master Degree or equivalent
• Languages: FRENCH; Level: Basic
• Research Field: Engineering; Years of Research Experience: None
• Research Field: Computer science; Years of Research Experience: None
• Research Field: Mathematics; Years of Research Experience: None

Additional Information

Website for additional job details: https://emploi.cnrs.fr/Offres/Doctorant/UMR5159-VIVGIO-008/Default.aspx

Work Location(s)

• Number of offers available: 1
• Company/Institute: Techniques de l'Informatique et de la Microélectronique pour l'Architecture des systèmes intégrés
• Country: France
• City: GRENOBLE

Contact

• City: GRENOBLE
• Website: https://tima.univ-grenoble-alpes.fr/