Network Traffic Flow Based Machine Learning Technique for IoT Device Identification
Bibliographic record
Abstract
Security and privacy issues are being raised as smart systems are integrated into our daily lives. New security issues have emerged with several new vendors that develop the Internet of Things (IoT) products. The contents and patterns of network traffic will expose vulnerable IoT devices to intruders. New methods of network assessment are needed to evaluate the type of network connected IoT devices. IoT device recognition would provide a comprehensive structure for the development of stable IoT networks. This paper chooses a machine learning technique to identify IoT devices linked to the network by analyzing network flow sent and received. To generate network traffic data, we have developed a dataset adapted from the IoT23 Pcap files to experiment with a smart home network. We have created a model to identify the IoT device based on network traffic analysis. We evaluate our proposed model via full features dataset, reduces features dataset, and flow-based features dataset. This paper focuses on using flow-based features to identify the IoT device connected to the network. Our proposed scheme results in 100% precision, precision, recall, and F score via a full features dataset, reduced features dataset, and flow-based features dataset. Through evaluations using our produced dataset, we demonstrate that the proposed model can accurately classify 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 | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".