A Dynamic Coding Scheme for Preventing Controllable Cyber-Attacks in Cyber-Physical Systems
Bibliographic record
Abstract
Controllable attacks are considered as perfectly undetectable cyber-attacks that are performed by compromising input communication channels of cyber-physical systems (CPS). They are referred to as perfectly undetectable since they have zero impact on the sensor measurements of the system. In this paper, we investigate conditions under which adversaries are capable of performing controllable cyberattacks and develop methods for designing these attack signals. Moreover, under certain assumptions, conditions for designing controllable attacks in terms of the Markov parameters of the CPS are derived. In order to analyze the vulnerability of the CPS to controllable attacks from the system operators’ point of view, a security metric designated as the security effort (SE) for controllable attacks is formally defined and proposed. The SE for controllable attacks denotes the minimum number of input communication channels that need to be secured to prevent adversaries from executing this type of cyberattack. Consequently, as a countermeasure, we develop a coding scheme on the input communication channels that increases the minimum number of required input communication channels for performing controllable attacks to its maximum possible value. Consequently, in presence of the proposed coding scheme, adversaries need to compromise all the input communication channels to execute controllable attacks. Therefore, securing only one input channel prevents adversaries from performing controllable cyber-attacks. Finally, an illustrative numerical case study is provided to demonstrate the effectiveness and capabilities of our derived conditions and proposed methodologies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".