Criteria for Securing Operating Systems Supporting Low-End Devices in the Internet of Things
Bibliographic record
Abstract
The Internet of Things (IoT) is now comprised of tens of billions of Internet-connected devices.As the IoT continues to grow in size and complexity, need for specialized IoT operating systems (OSs) becomes increasingly important to facilitate rapid development of secure and portable applications.However, providing such support for the subset of lowend devices called for in resource-constrained environments introduces additional design challenges, particularly with respect to security.In this thesis, we propose nine criteria to collectively encapsulate several important aspects and considerations for securing OSs supporting low-end devices in the IoT.To begin, we discuss key characteristics, use cases, and OS support for low-end devices in the IoT.For context, we also select and provide a summary of two actively developed IoT OSs with academic origins, RIOT and Tock.We then present our three main contributions, each of which builds upon one another to inform and end in our proposed IoT OS security criteria.First, we review the role of foundational hardware-and software-based mechanisms relating to OS security.We discuss the need for such mechanisms and identify several relating to IoT OSs supporting low-end devices, accompanying each with a case study pertaining to a real-world example.Second, we experimentally examine the use of such mechanisms in both of our selected IoT OSs running on an ARM Cortex-M based low-end device.Finally, we combine these contributions with a literature review to derive, support, and propose nine criteria for securing OSs supporting low-end devices, we further evaluate and compare each proposed criterion against the aforementioned IoT OSs.
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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 | 0.006 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".