Future of Automotive Embedded Hardware Trust Anchors (AEHTA)
Bibliographic record
Abstract
The current automotive electronic and electrical (EE) architecture has reached a scalability limit and in order to adapt to the new and upcoming requirements, novel automotive EE architectures are currently being investigated to support: a) an Ethernet backbone, b) consolidation of hardware capabilities leading to a centralized architecture from an existing distributed architecture, c) optimization of wiring to reduce cost, and d) adaptation of service-oriented software architectures. These requirements lead to the development of Zonal EE architectures as a possible solution that require appropriate adaptation of used security mechanisms and the corresponding utilized hardware trust anchors. 1 The current architecture approaches (ECU internal and in-vehicle networking) are being pushed to their limits, simultaneously, the current embedded security solutions also seem to reveal their limitations due to an increase in connectivity. In conjunction with an increasing number of related laws and regulations (such as UNECE R155 and ISO 21434), these drive security requirements in different domains and areas. 2 In this paper we examine the upcoming trends in EE architectures and investigate the underlying cyber-security threats and corresponding security requirements that lead to potential requirements for “Automotive Embedded Hardware Trust Anchors” (AEHTA). We see that communication requirements including increased feature set (such as Authenticated Encryption for Associated Data (AEAD) and Authentication of Associated Data (AAD)) and increased performance requirements significantly impact the architecture of AEHTA. Additionally, we try to point out how new smart solutions could improve overall performance of applications needing security mechanisms. 3 We show, that the overall impact of new security requirements on AEHTA can be categorized into security feature set, safety for security requirements and performance requirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.029 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".