Building components with embedded security monitors
Bibliographic record
Abstract
A software component should be trustworthy and behave in a secure manner as it will be reused many times. Despite extensive efforts, usually, it cannot be guaranteed that a developed software component is completely secure. Hence, its execution in the real-world needs to be monitored against its security specifications. Each time components are used to develop a component-based software (CBS), a new monitor has to be designed to observe the behavior of the CBS. This results in recurring costs as such monitors cannot be reused for other CBS. Moreover, development life cycle artifacts are usually not available when a pre-fabricated component is used to build a CBS. Given that, it is imperative that a specification-based security monitor is developed along with the monitored component (when all development artifacts are available) and is embedded in the component to increase the component's trustworthiness. In this paper, we identify the types of constraints that may be imposed by security specifications. These constraints should be taken into account while developing the software components and should also be monitored. Furthermore, we propose a design approach to develop components with built in monitors that are able to observe these security constraints. Components developed following this approach would be self-monitoring, promote greater reusability, and be more trustworthy. We evaluate our approach by analyzing the performance and design complexity of different versions of CBS. These versions are developed by following the traditional and proposed approaches for monitoring security aspects of CBS.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".