A Game-Theoretic Approach for Security Control Selection
Bibliographic record
Abstract
Selecting the combination of security controls that will most effectively protect a system's assets is a difficult task.If the wrong controls are selected, the system may be left vulnerable to cyberattacks that can impact the confidentiality, integrity and availability of critical data and services.In practical settings, it is not possible to select and implement every control possible.Instead considerations, such as budget, effectiveness, and dependencies among various controls, must be considered to choose a combination of security controls that best achieve a set of system security objectives.In this paper, we propose a game-theoretic approach for selecting effective combinations of security controls based on expected attacker profiles and a set budget.The control selection problem is set up as a two-person zero-sum one-shot game.Valid control combinations for selection are generated using an algebraic formalism to account for dependencies among selected controls.We demonstrate the proposed approach on an illustrative financial system used in government departments under four different scenarios.The results illustrate how a security analyst can use the proposed approach to guide and support decision-making in the control selection activity when developing secure systems.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".