ACIR: An Aspect-Connector for Intrusion Response
Bibliographic record
Abstract
The modularization concept behind component-based software (CBS) cannot be applied effectively for cross-cutting concerns such as security. Aspect-oriented programming (AOP) helps in better modularization by identifying cross-cutting concerns and providing a suitable way to separate those concerns. In this paper, we provide an aspect-connector based intrusion response (detection and prevention) architecture for CBS by bringing the concepts of aspects into components. The aspect-connector is named as ACIR (aspect connector for intrusion response). Component interfaces act as join points, and aspects containing pointcuts and advices are defined in ACIR configuration file. Advices applicable to particular pointcuts are two types. Signature advices are used to detect intrusions, and action advices are executed to prevent intrusions. A prototype of this architecture is implemented and evaluated using some intrusions included in the Web application security consortium (WASC) intrusion list. This approach detects and prevents intrusions in CBS while maintaining encapsulation, reusability, and modularity.
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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 | 0.004 | 0.006 |
| 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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".