Analyzing the Impact of Cyberattacks on Industrial Control Systems using Timed Automata
Bibliographic record
Abstract
Many of today's critical infrastructures, including industrial control systems (ICS), are evolving with the integration of numerous connected cyber components with legacy systems. This evolution has exposed ICSs to a new range of security vulnerabilities and threats, making cybersecurity considerations ever more critical. Understanding how severely malicious cy-berattacks can exploit such system vulnerabilities to disrupt or delay system operations is paramount for developing targeted and effective defenses. In this paper, a timed formal modelbased approach is presented to observe and analyze the manifold impact of various cyberattacks on ICS operations. The analysis is automated using UPPAAL on timed automata models of a target system and potential attackers. Representative tampering and spoofing attacks demonstrated on an illustrative manufacturing cell control system show how classical and statistical model checking, respectively, are effective at analyzing the existence and quantifying the severity of cyberattack impact on ICS mission objectives.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".