Research on Digital Campus Network Security Vulnerability Identification System Based on Numerical Analysis
Bibliographic record
Abstract
In the digital campus network security construction, the existence of potential security vulnerabilities can easily cause serious threats to campus information security, resulting in significant losses.In order to prevent and mitigate the risk, the article designs a security vulnerability identification system.Firstly, the URL similarity is compared by machine learning in order to scan the vulnerability information.The SeCF embedding layer is utilized to improve the input speed and the discard layer is designed to improve the overfitting problem during the training process.Finally, TextACBL security vulnerability identification model is proposed by combining CA, 1D-CNN and BiLSTM techniques and analyzed numerically.The average recognition rate of this paper's method is as high as 80% for 10 common security vulnerabilities, which achieves better security vulnerability recognition results compared with existing methods such as cppcheck, deepbugs, flawfinder and vuldeepecker.The experimental results verify the effectiveness and feasibility of the method in this paper, which provides ideas for safeguarding campus network security during the construction of digital campus.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".