A Self-Tuning Cyber-Attacks’ Location Identification Approach for Critical Infrastructures
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
The integration of the communications network and the Internet of Things in today’s critical infrastructures facilitates intelligent and online monitoring of these systems. However, although critical infrastructure’s digitalization brings tremendous advantages and opportunities for remote access and control, it significantly increases cyber-attack’s vulnerability. Therefore, efficient and proper detection and localization of cyber-attack are paramount for the critical infrastructure’s reliable and secure operation. This article proposes a deep learning-based cyber-attack detection and location identification system for critical infrastructures by constructing new representations and model the system behavior using multilayer autoencoders. The results show that the new representations capture the physical relationships among the measurements and have more discriminant power in distinguishing the location of the attack. Furthermore, the proposed method has outperformed conventional machine learning models under various cyber-attack scenarios using real-world data from the gas pipeline and water distribution supervisory control and data acquisition systems.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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