Transferability of Machine Learning Algorithm for IoT Device Profiling and Identification
Bibliographic record
Abstract
The lack of appropriate cyber security measures deployed on Internet of Things (IoT) makes these devices prone to security issues. Consequently, the timely identification and detection of these compromised devices become crucial. Machine learning (ML) models which are used to monitor devices in a network have made tremendous strides. However, most of the research in profiling and identification uses the same data for training and testing. Hence, a slight change in the data renders most learning algorithms to work poorly. In this article, we study a transferability approach based on the concept of transductive transfer learning for IoT device profiling and identification. Notably, this type of transfer learning works by explicitly assigning labels to the test data in the target domain by using the test feature space in the target domain, with training data from the source domain. Specifically, we propose a three-component system comprising: 1) the device type identification; 2) the vulnerability assessment; and 3) the visualization module. The device type identification component uses the underlying concept of transductive transfer learning where the trained model is transferred to a remote lab for testing. A variety of ML models are evaluated with respect to accuracy, precision, recall, and F1-score in order to determine which are the most suitable for the proposed transferability profiling. Furthermore, the vulnerability of the predicted device type is also assessed by using three vulnerability databases: 1) Vulners; 2) National Vulnerability Database (NVD); and 3) IBM X-Force. Finally, the results from the vulnerability assessment are visualized and displayed on a dashboard.
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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".