PhD Defence of Priyadarshini

It is our pleasure to announce the PhD defense of Priyadarshini. This defense is scheduled on Monday, November 25th, 2024 at 14:00, in room A042 at Esisar Engineering School, Valence.

Attending through Zoom is possible:

https://grenoble-inp.zoom.us/j/91446307201

ID de réunion: 914 4630 7201
Code secret: 533675

The defense jury members are:

• Mme Oum-El-Kheir AKTOUF, Professeure des Universités, GRENOBLE INP – UGA – Directrice de thèse
• Mme Fatiha ZAIDI, Professeure des Universités, Université PARIS-SACLAY – Rapportrice
• M. Salah SADOU, Professeur des Universités, Université BRETAGNE SUD – LORIENT VANNES – Rapporteur
• M. Andrea CECCARELLI, Associate Professor – Universita DEGLI STUDI DI FIRENZE – Examinateur
• M. Jean-Paul JAMONT, Professeur des Universités, Université GRENOBLE ALPES – Examinateur

This thesis has been prepared in the framework of a collaboration between LCIS Lab. and Robert Bosch GmbH, under the co-supervision of:

• Oum El Kheir AKTOUF Professeure des Universités, GRENOBLE INP – UGA – Directrice de thèse
• Maike MASSIERER, Automotive Security Expert, Robert Bosch GmbH – Co-encadrante de thèse
• Simon GREINER, Automotive Security Expert, Robert Bosch GmbH – Co-encadrant de thèse

Thesis title: Automated identification of behavioural interactions between safety and security features in automotive systems

Abstract: Today’s transportation systems are undergoing a major transformation, driven by electrification, enhanced connectivity, and the integration of software-defined features and machine learning algorithms. These advancements substantially increase system complexity and the risk of unintended feature interactions. The shift towards automated driving reduces human involvement, heightening the need for systems that ensure both safety and security.

While standards such as ISO 26262 for functional safety and ISO/SAE 21434 for cybersecurity set stringent requirements, there is no industry standard that addresses the interactions between safety and security artefacts. Unintended interactions between these artefacts can introduce significant risks, including critical safety concerns. These challenges are compounded by differing terminologies, separate development teams, and tight delivery timelines, often resulting in late detection of these interactions, leading to higher costs and delays.

The primary goal of this thesis is to develop methodologies for identifying safety and security interactions in the automotive domain, thereby enhancing overall system dependability. The figure above highlights the safety and security interactions that we address in this thesis with numbered blue arrows.

We propose a method to identify the causal relationship between the behavioural specifications of correctly implemented security features and system component failures (indicated as number 1 in the figure). Additionally, we introduce methods to detect behavioural interactions between functional safety and cybersecurity features during the software architecture design phase (illustrated by number 2 in the figure). By developing tools to automate these methods, we enable the early identification of interactions in complex automotive systems, facilitating the utilisation of synergies and the resolution of conflicts, and thereby enhancing system dependability and performance.