An Unsupervised Machine Learning Approach for IoT Device Categorization
Bibliographic record
Abstract
Internet of Things (IoT) along with the advances in the recently emerged Edge Computing environment, have allowed the introduction of new and very diverse applications that can facilitate our everyday life. However, one intrinsic characteristic of IoT is the heterogeneity of the IoT devices that are continuously connected and disconnected, creating a highly volatile communication environment. In addition to that, new types of IoT devices are constantly manufactured making a supervised categorization approach not applicable due to the lack of historical data. Nonetheless, the classification or type identification of the IoT devices is important for the management and the decision making of the IoT applications, and can be used for traffic characterization, density prediction, network planning and security reasons among others. Accordingly, in this paper we propose for the first time an unsupervised machine learning methodology for the IoT device categorization that leverages traffic characteristics obtained at the network level. To this end, we tackle the limitation of requiring an annotated dataset, while our model could also work efficiently with new and not previously detected IoT devices. To do so, we experimentally evaluate our approach using two clustering algorithms namely, the K-Means and the BIRCH in a real dataset. The experimental evaluation presents promising results that enhance the applicability of unsupervised approaches for the IoT device categorization problem.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".