Identification of IoT Devices Using A Multiple Transformers Single Estimator (MTSE) Learning Pipeline
Bibliographic record
Abstract
Adopting a malicious IoT device connected to a company’s network is a hazard to the enterprise. Firms must be able to distinguish between legitimate devices connected to their network and those that are threats. It is essential to comprehend the device’s properties and recognize it once it has been added to a network. Various machine learning algorithms for device identification have been introduced for this task, including Support vector machines, Adaboost classifiers, Logistic regression, KNeighbors classifiers, decision trees classifiers, XGBoost, Gradient boosting classifiers, and Random forests. However, there is a research gap in identifying the best-performing classification pipeline that adapts to the features and types of the associated data in the device identification problem while providing high recognition accuracy. For this purpose, we propose a Multiple Transformers Single Estimator (MTSE) Learning pipeline that captures the best-performing feature sets and the best-performing machine learning algorithm for the recognition problem. The MTSE uses transformers and estimators; the transformers filter or modify the data. The estimator learns and builds a recognition model from the data. Experimental results through different transformers and estimators achieved an accuracy of 89.50% compared to the existing classification methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".