Economics of Security Patch Management.
Bibliographic record
Abstract
Patch management is a crucial component of IT security programs. An important problem within this context is to determine how often to update the systems with necessary patches. Keeping the systems patched with more frequent patch updates increases operational costs while reducing security risks. On the other hand, leaving the systems unpatched with less frequent patch updates decreases operational costs while increasing security risks. In this paper we develop a game theoretic model to derive the optimal frequency of patch updates to balance the operational costs and damage costs associated with security vulnerabilities. We first analyze a centralized system in a benchmark case to find the socially optimal patch management policy and associated patch release cycle of the vendor and patch update cycle of the firm. Then we consider a noncentralized system in which the vendor determines its patch release policy and the firm selects its patch update policy in a Stackelberg framework. Given the results in centralized and noncentralized patch management, we next address how we can coordinate the patch release policy of the vendor and the patch update policy of the firm using cost sharing and/or liability to achieve the socially optimal patch management in a noncentralized setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".