MétaCan
Menu
Back to cohort
Record W3137191745 · doi:10.1145/3422337.3447834

The Cost of OSCORE and EDHOC for Constrained Devices

2021· preprint· en· W3137191745 on OpenAlexaff
Stefan Hristozov, Manuel Huber, Lei Xu, Jaro Fietz, Marco Liess, Georg Sigl

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicSecurity and Verification in Computing
Canadian institutionsMicrosoft (Canada)
Fundersnot available
KeywordsFirmwareComputer scienceEncryptionMicrocontrollerExploitAuthenticated encryptionEmbedded systemComputer securityProtocol (science)Payload (computing)Computer networkBuffer overflowThe InternetOperating system

Abstract

fetched live from OpenAlex

Many modern IoT applications rely on the Constrained Application Protocol (CoAP). Recently, the Internet Engineering Task Force (IETF) proposed two novel protocols for securing it. These are: 1) Object Security for Constrained RESTful Environments (OSCORE) providing authenticated encryption for the CoAP's payload data and 2) Ephemeral Diffie-Hellman Over COSE (EDHOC) providing the symmetric session keys required for OSCORE. In this paper, we present the design of four firmware libraries for these protocols which are especially targeted for constrained microcontrollers and their detailed evaluation. More precisely, we present the design of uOSCORE and μEDHOC libraries for regular microcontrollers and μOSCORE-TEE and μEDHOC-TEE libraries for microcontrollers with a Trusted Execution Environment (TEE), such as microcontrollers featuring ARM TrustZone-M. Our firmware design for the latter class of devices concerns the fact that attackers may exploit common software vulnerabilities, e.g., buffer overflows in the protocol logic, OS or application to compromise the protocol security. We present an evaluation of our implementations in terms of RAM/FLASH requirements and execution speed on a broad range of microcontrollers. Our implementations are available as open-source software.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.912
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.303
Teacher spread0.258 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicSecurity and Verification in ComputingFrench-language works237,207