Bibliographic record
Abstract
The proliferation of Internet of Things (IoT) devices has significantly advanced automation and connectivity across various domains, including smart homes, cities, industry, agriculture, transportation, and healthcare. However, this widespread adoption also presents substantial management and security challenges. In smart home environments, the limited computational capabilities and lack of robust encryption mechanisms in IoT devices make them prime targets for cyberattacks. Accurate identification of IoT devices is essential for implementing effective cybersecurity measures. Previous studies have proposed various approaches for identifying IoT devices; however, these studies are constrained by complex networks under strict conditions, with a small number of IoT devices in the datasets and variations in implementations. This study introduces ScanIoT, an easily deployable data collection framework designed to capture IoT device traffic in smart homes. Using this framework, we collected the Concordia University IoT device identification dataset (CU2025). We also propose a DFNN model that achieves an accuracy of $\mathbf{9 9 . 9 7 \%}$ for the device category classification and 99. 73% for the device classification on the CICIoT2023 dataset. DFNN achieves an accuracy of $\mathbf{9 9 . 9 9 \%}$ on the CU2025 dataset for device classification.
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".