Retraction Notice A Hybrid Multistage DNN-Based Collaborative IDPS for High-Risk Smart Factory Networks
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
New industrial control systems (ICSs) that have been modernized with the industrial Internet of Things (IIoT) are exposed to cyber-attacks that exploit IIoT vulnerabilities. Numerous intrusion detection systems (IDSs) have therefore been proposed to secure ICSs, many of which are based on machine learning, specifically deep neural networks (DNNs). Most of the proposed DNN-based solutions rely on single deep learning models and could be less costly in terms of ICS latency. However, they might have difficulties understanding the increasingly complex data distribution of intrusion patterns. Moreover, single deep learning models may not be effective in capturing the specific patterns of minority classes in highly imbalanced datasets, which is usually the case in cyber-security. Therefore, this paper proposes a novel hybrid multistage DNN-based intrusion detection and prevention system (IDPS) with better accuracy for critical ICSs that cannot afford to compromise on security to improve latency. The proposed approach sequentially learns the decision boundaries of the data that were misclassified or classified with low confidence by previous DNNs. Moreover, it incorporates a collaborative intrusion prevention system (IPS) with an emergency response schema that automatically mitigates attacks as soon as anomalies are detected. The results of experimental evaluations performed on different datasets demonstrate the effectiveness ofthe proposed solution.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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