2021-04099 - PhD Position F/M Labex Persyval of the Université Grenoble-Alpes - Patch analysis of IIoT components
Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Bac + 5 ou équivalent

Fonction : Doctorant

A propos du centre ou de la direction fonctionnelle

Labex Persyval of the Université Grenoble-Alpes

Contexte et atouts du poste

This problem is addressed by the D-IIoT project, supported by the Labex Persyval of the Université Grenoble-Alpes, and gathering several partners (LIG, INRIA Rhône-Alpes, Verimag and CEA LIST).

The grant is provided by the University Grenoble-Alpes via the Labex Persyval. The amount corresponds to a standard French PhD grant.

PERSYVAL-lab federates 800 researchers and academics from 10 laboratories in Grenoble working on computer science, hardware architecture, signal processing, control and mathematics towards a common scientific goal: build secure, reliable and efficient cyber-physical systems combining "smart" devices interconnected and interactive virtual objects.

Mission confiée

In this broad context, this PhD objective is to develop a patch analysis framework able to predict and anticipate the behavioral when upgrading the code of an IIoT component. This impact should encompass both safety and security properties.

Principales activités

The approach proposed is to leverage existing techniques (like shadow symbolic execution [1] and differential fuzzing [2]) in this specific application context of close-source networked embedded systems. The analysis will be partly driven by some formal models of the whole IIoT environment, and it will rely on reverse engineering, code instrumentation and monitoring techniques to obtain a faithful enough dynamic analysis environment. The expected outcomes are dedicated patch analysis techniques and methodologies for IIoT applications, together with a tool prototype. Case studies will be provided by the D-IIoT project and will consist in industrial applications as those used in smart grids or manufacturing plants.

 

 

References:

 

[1] Tomasz Kuchta, Hristina Palikareva, and Cristian Cadar.

Shadow symbolic execution for testing software patches.

ACM Trans. Softw. Eng. Methodol., 27(3):10:1–10:32, 2018.

 

[2] Yannic Noller, Corina Pasareanu, Marcel Böhme, Youcheng Sun, Hoang Lam Nguyen, and Lars Grunske. Hydiff: Hybrid differential software analysis. In 42st International Conference on Software Engineering, 2020.

 

Avantages

Social security coverage

Rémunération

The grant is provided by the University Grenoble-Alpes via the Labex Persyval. The amount corresponds to a standard French PhD grant.