Comparing deep neural networks to tree-based machine learning methods for anomaly detection in IIoT
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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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".