EVDD - A Novel Dataset For Embedded System Vulnerability Detection Mechanism
Bibliographic record
Abstract
In the digital world where the smart device is a ubiquitous feature of modern life, embedded systems are almost everywhere. From the smart home, to the smart office, to the smart restaurant, and even the smart traffic system, this technology has become a crucial aspect of the modern experience. Although these systems are typically quite efficient, the rapid growth of embedded systems in smart cities has created inconsistencies and misalignments between secured and unsecured systems, which has created a need for secure, invulnerable embedded systems to protect modern users from the dangers of hacking. To address this problem, we have developed a large, novel, and unbiased dataset for embedded system vulnerability detection and modifying an advanced machine learning model for Linux Kernel. This unique dataset will provide the landscape of embedded systems with a multitude of ways to predict the vulnerabilities in the Linux Core and to detect all vulnerabilities during the development process. This research paper discusses the collection, filtering, correlation, and statistics associated with the dataset for Linux Embedded 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 | 0.001 | 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 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".