Bibliographic record
Abstract
Many of today's critical infrastructures, including Industrial Control Systems (ICSs), are evolving with the integration of numerous connected cyber components with legacy systems.This evolution has exposed ICS to a wide range of security vulnerabilities, requiring different cybersecurity considerations.Understanding how severely cyberattacks can exploit such system vulnerabilities to disrupt or delay system operations is paramount for developing targeted and effective defenses.In this thesis, we present a four-stage impact analysis approach to observe and characterize the manifold impact of cyberattacks on ICS operations.Representative tampering and spoofing attacks demonstrated on a timed automata model of a manufacturing cell control system show how classical and statistical model checking, respectively, are effective at identifying and quantifying the severity of cyberattack impact on ICS mission objectives.Furthermore, the impact analysis results provide extensive insight into the varying impact caused by different attackers and how systems with defenses can mitigate such impacts.iii This work began with a collection of fragmented ideas on viewing system security from a different perspective, which has since evolved into something much more.The work itself, and my growth during its progress, would not be possible without the sincere and unwavering support from my supervisor Professor Jason Jaskolka.His guidance, most times as a supervisor but at times as a guardian or even an elder brother, has helped me pull through the hardest of times.I am grateful beyond words.I thank my colleagues for creating an earnest yet relaxing environment to blend in.A very special thank you goes to Joe Samuel for his overwhelmingly optimistic influence, and more importantly, for simply being a great friend.I express my sincere gratitude to Dr. Mohammad T. Kawser, who, even during his unimaginably agonizing moments, guided me to explore the fundamentals of research.I
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".