Onboarding and Software Update Architecture for IoT Devices
Bibliographic record
Abstract
The vast number of in-use Internet of Things (IoT) devices is by consensus, expected to continue rapid growth. These devices are subject to an expanding list of attacks that exploit both software vulnerabilities and design choices. This highlights the importance of architectural design of management for cryptographic keys involved in both initial configuration (onboarding) and secure, automatic update of device software and firmware. Low-level IoT devices with constrained processors and smaller registers and caches are computationally challenged to carry out desktop- and server-type public-key cryptographic operations, e.g., as needed for key establishment and authentication of software updates. To this end, we design and prototype an architecture for onboarding and secure software update of low-level IoT devices (8-bit). It uses elliptic curve cryptography (Curve25519), authenticated key establishment, and a known continuity-based key-locking mechanism that uses a public key embedded in a current software image to verify the signature on a software update. We also provide an informal security analysis. The design addresses the scenario of a transfer of update authority, e.g., when a manufacturer ceases to provide ongoing software updates upon going out of business.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".