Security Vulnerabilities Tests Generation from SysML and Event-B Models for EMV Cards
Bibliographic record
Abstract
The Model Based Testing (MBT) is an original approach where test cases are automatically generated from the specifications of the system under tests. These specifications take the form of a behavioral model allowing the test generator to determine, on the one hand, the possible and relevant execution contexts. On the other hand, to predict the effects of these executions on the system. This paper proposes new methodology to generate vulnerability test cases based on SysML model of Europay-Mastercard and Visa (EMV) specifications. Our main aim is to ensure that not only the features described by the EMV specifications are met, but also that there is no vulnerability in the system. To meet these two objectives, we automatically generated concrete tests basing on SysML models. Indeed, this paper highlights the importance of modeling EMV specifications. We opted for the choice of SysML modeling language due to its ability to model Embedded Systems through several types of diagrams. In our work we used state machine diagram to generate vulnerability test cases for a secure and robust system.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".