Security and Machine Learning Adoption in IoT: A Preliminary Study of\n IoT Developer Discussions
Bibliographic record
Abstract
Internet of Things (IoT) is defined as the connection between places and\nphysical objects (i.e., things) over the internet/network via smart computing\ndevices. Traditionally, we learn about the IoT ecosystem/problems by conducting\nsurveys of IoT developers/practitioners. Another way to learn is by analyzing\nIoT developer discussions in popular online developer forums like Stack\nOverflow (SO). However, we are aware of no such studies that focused on IoT\ndevelopers' security and ML-related discussions in SO. This paper offers the\nresults of preliminary study of IoT developer discussions in SO. We find around\n12% of sentences contain security discussions, while around 0.12% sentences\ncontain ML- related discussions. We find that IoT developers discussing\nsecurity issues frequently inquired about how the shared data can be stored,\nshared, and transferred securely across IoT devices and users. We also find\nthat IoT developers are interested to adopt deep neural network-based ML models\ninto their IoT devices, but they find it challenging to accommodate those into\ntheir resource-constrained IoT devices. Our findings offer implications for IoT\nvendors and researchers to develop and design novel techniques for improved\nsecurity and ML adoption into IoT devices.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".