Anomaly Detection for IoT Networks: Empirical Study
Why this work is in the frame
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Bibliographic record
Abstract
The Internet of Things (IoT) actively transforms physical objects, including portable, wearable, and implantable sensors, into an information ecosystem that enriches the technology and data in every aspect of life. This paper examines two anomaly detection approaches: novelty and outlier, for IoT networks. In this respect, we leverage four unsupervised learning algorithms, namely Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OSVM), and variational encoder (AE), on four publicly available IoT datasets. The experiments reveal that by embracing the novelty approach by considering only pure benign data for training, the AE model achieves a high F1-score and AUC up to 97% and 0.97.
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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 | 0.001 |
| 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