Towards Safe and Secure Resource-Constrained In-Vehicle Real-Time Embedded Systems

We consider safety-critical real-time embedded systems with hardware resource constraints, e.g., embedded systems in Autonomous Vehicles (AVs),  which may come in different shapes and sizes with different levels of resource constraints.

The overarching objective of the project is to develop techniques and tools to help the system designer in the implementation of safe and secure in-vehicle  real-time embedded systems subject to multiple levels of safety certification, in the presence of HW resource constraints, including processor speed, memory size, and energy consumption.

The project consists of two tasks that work together to achieve the project goal: Task 1 concerns the issue of safety and security within each ECU node, and addresses resource-constrained MCS based on ARM TrustZone technology.

We plan to leverage TrustZone for strong isolation of on-chip HW resources (e.g., I/O, cache, memory, and interconnects) between different criticality levels, and develop optimization algorithms for minimizing stack memory size and energy consumption. Task 2 concerns the issue of security at the networked system level, and addresses resource-constrained in-vehicle IDS.

We plan to develop Machine-Learning-based IDS for industry-standard in-vehicle network protocols (esp.

Time-Sensitive Networking (TSN)),  focusing on timing performance benchmarking and optimization of  Anomaly Detection algorithms.