Interplay of Attacker Behaviors and Dependability Attributes in Industrial Control System Impact Analysis
Bibliographic record
Abstract
Industrial control systems (ICS) are increasingly vulnerable to cyberattacks that can propagate to impact physical industrial processes. Existing research on ICS impact analysis views ICS dependability attributes as an afterthought and focuses primarily on attacks but not the attackers and their different behaviors. In this work, we include explicit considerations for ICS dependability attributes and attacker behaviors in ICS impact analysis. By adopting the Structured Cyberattack Impact Analysis (SCIA) approach, our model-based impact analysis is demonstrated on a manufacturing ICS modeled in UPPAAL-SMC. More specifically, we visualize and quantify, respectively, using simulations and statistical model checking, the potential impact of data tampering attacks when performed by attackers with different behaviors (random, relentless, and informed). Furthermore, the impact analysis results highlight the interplay of ICS dependability attributes in terms of (1) how attacks on ICS security can impact system reliability and availability, (2) how improving security can improve system availability and reliability, and (3) how accepting some sacrifices on one attribute (availability) can end up improving other attributes (security and reliability).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".