Intrusion Detection in IIoT Using Machine Learning
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
In the Industrial Internet of Things (IIoT), leveraging Internet of Things (IoT) technologies such as machines, sensors, and software in industrial applications has been instrumental in enhancing productivity. However, the inherent vulnerability of IIoT systems to cyber-attacks poses significant threats to critical infrastructure and security. This paper explores the improvement of IIoT intrusion detection with ML techniques, using supervised models such as Random Forest and Decision Tree on the NF-UNSW-NB15-v2 dataset. SMOTE is applied to balance the data and improve accuracy, recall, and F1-Score. Two approaches, a multiclass classification and a binary classification followed by a multiclass, are evaluated via performance metrics. This study highlights the potential of machine learning to enhance IIoT security and highlights the importance of data balance in intrusion detection systems.
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.
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 | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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