A game oriented approach to minimizing cybersecurity risk
Bibliographic record
Abstract
systems are now ubiquitous in all aspects of our society. With an ability to create ICT incident effects via cyberspace, criminals can steal information or extort money, terrorists can disrupt society or cause loss of life, and the effectiveness of a military can be degraded. These threats have caused an imperative to maximize a system's cyber security resilience. Protecting systems that rely on ICT from cyber-attacks or reducing the impacts that cyber incidents cause is a topic of major importance. In this paper, we describe an approach to minimizing cybersecurity risks called Cyber Security Game (CSG), where CSG can be viewed as a form of model-based system security engineering. CSG is a method and supporting software that quantitatively identifies mission outcome focused cybersecurity risks and uses this metric to determine the optimal employment of security methods to use for any given investment level. CSG maximizes a system's ability to operate in today's contested cyber environment by minimizing its mission risk. The risk score is calculated by using a cyber mission impact assessment (CMIA) model to compute the consequences of cyber incidents, and by applying a threat model to a system topology model and defender model to estimate how likely attacks are to succeed. CSG takes into account the widespread interconnectedness of cyber systems, where defenders must defend all multi-step attack paths and an attacker only needs one to succeed. It employs a game theoretic solution using a game formulation that identifies defense strategies to minimize the maximum cyber risk (MiniMax), employing the defense methods defined in the defender model. This paper describes the approach and the models that CSG uses.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".