Automated Extraction of IoT Critical Objects from IoT Storylines, Requirements and User Stories via NLP
Bibliographic record
Abstract
The first step to designing a resilient Internet of Things (IoT) application is to identify IoT critical objects (services, devices and resources) in the design phase. However, this step is a time-intensive task, because they are manually identified from storylines, requirements and user stories and have other challenges. In this work, we assessed the usefulness of Named Entity Recognition (NER) models to automatically identify IoT critical objects as a way to make a modelling process faster and less prone to errors. This was performed with the development of five NER models based on five different architectures (Spacy, BERT, Transformers, LSTM-CRF and ELMo) that were trained and tested with a large dataset with 7396 annotated sentences. Our results indicate that all NER models had satisfactory performance, but BERT had the best one and can be useful to support the time-intensive step of the early stages of the development of resilient IoT systems. Furthermore, these NER models have a high potential to be extended to a framework to automatically extract IoT critical objects from documents (storyline and requirements) and list all possible IoT threats and resilient countermeasures that can be used in the design of a resilient IoT application.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".