UMLintr: a UML profile for specifying intrusions
Bibliographic record
Abstract
Specifications of non-functional requirements (NFR) such as security, safety, usability are as important as specification of functional requirements (FR). Non conformance to some NFR may render the whole software useless. There are many difficulties associated with the representation of NFR and the complexity of their subsequent validation. The main objective of this work is towards incorporating an important aspect of NFR, i.e., security from the very beginning of a software development process. In this paper, a framework is presented for specifying intrusion scenarios in the Unified Modeling Language (UML). We describe a UML profile called UMLintr (UML for intrusion specifications) that allows developers to specify intrusions using UML notations extended to suit the context of intrusion scenarios. The framework utilizes the expressiveness of UML and eliminates the need of using attack languages that are proposed only to describe attack scenarios. Since developers do not need to learn a separate language to describe attacks, the task of specifying intrusion scenarios becomes much easier. This approach also helps to avoid conflicting (e.g., security vs. usability), ambiguous, and redundant requirements. Examples are provided to show the usage of the proposed UML profile.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".