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2020-12-02
Niz, D. de, Andersson, B., Klein, M., Lehoczky, J., Vasudevan, A., Kim, H., Moreno, G..  2019.  Mixed-Trust Computing for Real-Time Systems. 2019 IEEE 25th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA). :1—11.

Verifying complex Cyber-Physical Systems (CPS) is increasingly important given the push to deploy safety-critical autonomous features. Unfortunately, traditional verification methods do not scale to the complexity of these systems and do not provide systematic methods to protect verified properties when not all the components can be verified. To address these challenges, this paper proposes a real-time mixed-trust computing framework that combines verification and protection. The framework introduces a new task model, where an application task can have both an untrusted and a trusted part. The untrusted part allows complex computations supported by a full OS with a realtime scheduler running in a VM hosted by a trusted hypervisor. The trusted part is executed by another scheduler within the hypervisor and is thus protected from the untrusted part. If the untrusted part fails to finish by a specific time, the trusted part is activated to preserve safety (e.g., prevent a crash) including its timing guarantees. This framework is the first allowing the use of untrusted components for CPS critical functions while preserving logical and timing guarantees, even in the presence of malicious attackers. We present the framework design and implementation along with the schedulability analysis and the coordination protocol between the trusted and untrusted parts. We also present our Raspberry Pi 3 implementation along with experiments showing the behavior of the system under failures of untrusted components, and a drone application to demonstrate its practicality.

2018-05-09
Fellmuth, J., Herber, P., Pfeffer, T. F., Glesner, S..  2017.  Securing Real-Time Cyber-Physical Systems Using WCET-Aware Artificial Diversity. 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). :454–461.

Artificial software diversity is an effective way to prevent software vulnerabilities and errors to be exploited in code-reuse attacks. This is achieved by lowering the individual probability of a successful attack to a level that makes the attack unfeasible. Unfortunately, the existing approaches are not applicable to safety-critical real-time systems as they induce unacceptable performance overheads, they violate safety and timing guarantees, or they assume hardware resources which are typically not available in embedded systems. To overcome these problems, we propose a safe diversity approach that preserves the timing properties of real-time processes by controlling its impact on the worst case execution time (WCET). Our main idea is to use block-level diversity with a large, but fixed set of movable instruction sequences, and to use static WCET analysis to identify non-critical areas of code where it can safely be split into more movable instruction sequences.