Issues and Challenges in Machine Learning-based IoT Device Discovery: An Empirical Study
Bibliographic record
Abstract
IoT device discovery and identification is a requisite part of ensuring strong cyber security posture for homes, organizations & industries. Increased use of encryption by such devices poses a present and future challenge for traditional identification methods based on Deep Packet Inspection (DPI). Recent research has proposed the use of AIbased approaches as a solution. However, enabling such systems to operate accurately and scalably across heterogeneous IoT devices and networks remains a challenge. This paper makes multiple contributions to advance the state of the art. First, we analyze representative domain research and summarize results. Second, we discuss pending issues and challenges which emerge from existing research. Third, we experimentally investigate and validate the pending challenges using three public datasets as well as two private datasets with 30 IoT devices. Our findings indicate that while research around per-device IoT ML models has advanced significantly, further research is required in the area of generalized AI models which carry out accurate discovery & identification across different $I o T$ devices and networks. We identify in particular, the area of device category identification as deserving of further research attention.
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 | 0.000 | 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 | 0.001 | 0.001 |
| 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".