Decision Support for Combining Security Mechanisms using Exploratory Evolutionary Testing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a process utilizing an evolutionary learning method to explore combinations of security mechanisms with regard to performance problems they might create for a particular user profile. For each combination, the process uses an evolutionary search to identify sequences of interactions with a computer (in form of a virtual machine) that stress the system to a much larger degree with the combination installed than without it. The process then compares the mechanism combinations using the “best sequences” for each combination to suggest the combination that overall has the least impact on performance. The process also explores interaction sequences that caused system failure, or were not able to finish within the given time limit, to identify incompatibilities between security mechanisms. For evaluation, the process was applied to create a tool for finding the best set of multiple anti-virus software systems for Windows XP. In the primary evaluation, the tool identified a set of five mechanisms that did not degrade performance too far, while providing the intended security coverage. At the same time, the tool found a clear incompatibility between two mechanisms as demonstrated by a zip operation failure after only a few interactions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it