IoT Device and State Identification based on Usage Patterns
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we explore usage patterns for the identification of IoT devices and their corresponding states. Machine Learning (ML) methods are trained on IoT device traffic patterns to recognize the state that the device is in. Three device states are the focus of this study - Power-up, Idle and Active. Devices are visible and open to cyber attacks from the moment they are powered on. Previous studies have focused primarily on identifying IoT devices which are in the active state. This study advances the research domain by exploring all three states of an IoT device. Eight different ML algorithms are evaluated using three different feature sets extracted from device network traffic, using flow analysis tools - Tranalyzer2, NFStream and Zeek. They are rigorously assessed to accurately identify diverse IoT devices under normal operational conditions over the aforementioned three states. .
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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 | 0.000 | 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 it