ACIR: An Aspect-Connector for Intrusion Response
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
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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