Identifying IoT Devices: A Machine Learning Analysis Using Traffic Flow Metadata
Bibliographic record
Abstract
Deployment of IoT (Internet of Things) devices in homes, corporate networks and industrial settings continues to rise. The weak security in many such devices leaves them susceptible to targeted attacks. As a result, there is a strong requirement to easily discover and identify such devices, even when they utilize encrypted communications. In this paper, we explore a machine learning (ML) based approach for identification of IoT devices. We contribute to this area of active research by developing a testbed consisting of nine IoT devices and create a labeled dataset which is publicly available. We further investigate eight different ML algorithms with features derived from two different open source network traffic flow analysis tools - Tranalyzer2 and NFStream. Three different flow metadata based feature sets were evaluated to determine their efficacy in identifying the different IoT devices during regular operation. The results show that the Random Forest ML model achieves the highest performance using the Tranalyzer2 feature set with a 99.68% (approximately 100%) F1-Score for identifying IoT devices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".