SECRApr 8, 2014

Towards the Model-Driven Engineering of Secure yet Safe Embedded Systems

arXiv:1404.1985v122 citations
Originality Incremental advance
AI Analysis

This work addresses the problem of balancing security and safety in embedded systems for designers and security experts, though it appears incremental as it builds on existing SysML frameworks.

The authors tackled the challenge of integrating security and safety requirements in embedded system design by introducing SysML-Sec, a Model-Driven Engineering environment that enables early hardware/software partitioning analysis with security considerations, resulting in a toolchain that formally verifies security properties through model transformation to ProVerif.

We introduce SysML-Sec, a SysML-based Model-Driven Engineering environment aimed at fostering the collaboration between system designers and security experts at all methodological stages of the development of an embedded system. A central issue in the design of an embedded system is the definition of the hardware/software partitioning of the architecture of the system, which should take place as early as possible. SysML-Sec aims to extend the relevance of this analysis through the integration of security requirements and threats. In particular, we propose an agile methodology whose aim is to assess early on the impact of the security requirements and of the security mechanisms designed to satisfy them over the safety of the system. Security concerns are captured in a component-centric manner through existing SysML diagrams with only minimal extensions. After the requirements captured are derived into security and cryptographic mechanisms, security properties can be formally verified over this design. To perform the latter, model transformation techniques are implemented in the SysML-Sec toolchain in order to derive a ProVerif specification from the SysML models. An automotive firmware flashing procedure serves as a guiding example throughout our presentation.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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