Comparing deep neural networks to tree-based machine learning methods for anomaly detection in IIoT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper investigates the application of machine learning methods for anomaly detection of both physical and cyber threats in Industrial Internet of Things (IIoT) environments, with a novel method of separating different threat classes, performing delegation of computationally inexpensive threshold-based metrics to a simple rules-based alerting system, while performing anomaly detection of the more complex behavioural-based metrics in a machine learning model. This hybrid approach of separating threshold-based and behaviour-based detection methods is validated on the Edge-IIoTset2023 and CICIoT2023 public research datasets. As a new contribution, this hybrid methodology is validated against both tree-based classifiers and artificial neural network (ANN) classifiers. Experimental results indicate that while ANNs can be very effective, marginally higher accuracy (~3%) and significantly faster predictions can be achieved with less computationally expensive tree-based algorithms such as Decision Trees and Random Forests, thereby optimizing the price-performance trade-off for the operators of IIoT environments.
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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 | 0.001 | 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