AsmLSec: An Extension of Abstract State Machine Language for Attack Scenario Specification
Bibliographic record
Abstract
Security, one of the most important aspects of software, gets very little attention during the software development life cycle (SDLC). Therefore, the software remains vulnerable to attacks which are handled by issuing patches or service packs by the software vendors. To overcome this problem, researchers have proposed to take security into consideration right from the very beginning of the software development process. However, most specification languages were not designed with an intention for specifying security requirements, and therefore, they lack some features to serve this purpose. As a result, we need suitable specification languages that can be used both for functional specification and security specification. We propose a formal extension of a popular specification language called AsmL (Abstract State Machine Language) for attack descriptions with a view to building secure software. We name the extended language AsmLSec. We present the details of AsmLSec syntax and semantics, describe how to model attacks using its constructs, and present the design and implementation of a compiler that generates attack signatures from the AsmLSec attack specifications. To evaluate the expressive power of AsmLSec, we model attack scenarios based on the benchmark DARPA data sets
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".