Dynamic Attack Scoring Using Distributed Local Detectors
Bibliographic record
Abstract
Nowadays, continuously operating critical services increasingly rely on complex cyber-physical systems, which are also known as high-profile targets of cyberattacks, potentially resulting in security breaches that can cause severe damage. This paper presents a novel study on detecting cyberattacks against distributed supervisory control systems. AttackTracker, a scalable and unsupervised analytic framework for behavior-based online intrusion detection, is organized as a hierarchical network of cooperating attack detectors. Each local attack detector monitors and reports the status of a subsystem by labeling observations, assigning attack scores, and raising red flags by comparing actual versus predicted signal values from the observed input stream. While higher-level detectors utilize information aggregated from detectors at lower levels to assess the global security status of the supervisory control system. Our experiments show that AttackTracker outperforms leading methods for detecting complex attacks in a real-world operational context and it can be used for intrusion detection across a wide range of cyber-physical systems.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".