Security Monitoring of Components Using Aspects and Contracts in Wrappers
Bibliographic record
Abstract
The re-usability and modularity of components reduce the cost and complexity of the software design. It is difficult to predict run-time scenarios covering all possible circumstances to ensure that the components are fully compatible with the system. Given that, monitoring run-time behaviours of components presents a close view of the component qualities. The existing monitoring approaches either implement applications with built-in monitoring features, or observe the external resources and events to predict the status of the components. In this paper, we propose an approach to monitor the runtime behaviours of components using aspect-oriented wrappers and contracts. We design monitoring wrappers to encapsulate the monitored components. We use contracts to define the mutual obligations of two interacting components. The policies implemented in contracts are woven into component wrappers as separate aspect modules. If the component contains any flaws or vulnerabilities, the wrappers can monitor some behaviours and prevent failures propagating into the wrapped components and the rest of the system. This approach assures that the system is running in a safe environment with the erroneous behaviours detected appropriately. We conducted experiments on the run-time monitoring of SQL Injection, Cross Site Scripting attacks, and access control policies. The results show that the framework is very flexible to impose separate policies as aspects on component wrappers without the modifications of the underlying components.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".