Using Exploratory Testing for Decision Support in Choosing a Security Mechanism
Bibliographic record
Abstract
From the point of view of a user, a security mechanism for a computer should protect it from the particular kind of attacks it is designed for, while influencing the performance of the computer for the user's applications as little as possible. In this paper, we present an evolutionary learning approach for exploratory testing of the performance consequences of a security mechanism on the user's usage profile, i.e. the applications the user is using.By learning application interaction sequences with performances that are the most negatively influenced by the installation of a security mechanism, a user can evaluate if the performance losses are acceptable and by applying our approach to several mechanisms with comparable protection, a user can make an informed decision which mechanism is better for him/her. As proof-of-concept, we used our approach to explore anti-virus security mechanisms operating in a Windows XP environment. Our experiments show that different usage profiles are indeed better served by different security mechanism.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".