Evaluating security products with clinical trials
Bibliographic record
Abstract
One of the largest challenges faced by purchasers of security products is evaluating their relative merits. While customers can get reliable information on characteristics such as runtime overhead, user interface, and support quality, the actual level of protection provided by different security products is mostly unranked—or, worse yet, ranked using criteria that do not generally reflect their performance in practice. Even though researchers have been working on improving testing methodologies, given the complex interactions of users, uses, evolving threats, and different deployment environments, there are fundamental limitations on the ability of lab-based measurements to determine real world performance. To address these issues, we propose an alternative evaluation method, computer security clinical trials. In this method, security products are deployed in randomly selected subsets of targeted populations and are monitored to determine their performance in normal use. We believe that clinical trials can provide solid evidence of the efficacy of security products, much as they have in the field of medicine. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.310 | 0.472 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| 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".