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Record W4220959531 · doi:10.4271/2022-01-0122

Future of Automotive Embedded Hardware Trust Anchors (AEHTA)

2022· article· en· W4220959531 on OpenAlex
Rolf Schneider, K.‐H. Schmidt, Udo Dannebaum

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2022
Typearticle
Languageen
FieldComputer Science
TopicPhysical Unclonable Functions (PUFs) and Hardware Security
Canadian institutionsInfineon Technologies (Canada)
Fundersnot available
KeywordsAutomotive industryComputer scienceEmbedded systemAutomotive electronicsComputer hardwareEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

<div class="section abstract"><div class="htmlview paragraph">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.<ol class="list nostyle"><li class="list-item"><span class="li-label">1</span><div class="htmlview paragraph">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.</div></li><li class="list-item"><span class="li-label">2</span><div class="htmlview paragraph">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.</div></li><li class="list-item"><span class="li-label">3</span><div class="htmlview paragraph">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.</div></li></ol></div></div>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0040.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.234
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it