Bibliographic record
Abstract
Given the rapid advancement of web applications and the increasing importance of web security, it is crucial to prioritize web security as a fundamental aspect of ensuring a secure online environment. Web apps are becoming more and more popular, but with that comes a wider range of security threats that can cause more serious harm. To safeguard web apps and discourage cyber-attacks, numerous research have been conducted. Some security issues that might affect web applications are provided in this article. SQL injection attacks are the most prominent attacks on web applications. These attacks could allow third parties to access private information without authorization, change or remove data, or even take down whole websites and databases. Traditional methods of detecting and neutralizing SQL injection attacks are often resource-intensive, making them impractical for devices handling large volumes of traffic. Examining and evaluating blockchain and AI-based technologies for improving web application security against “structured query language injection” (SQLI) threats is the goal of this Study. A literature review has been conducted to find the applications of blockchain and AI technologies in web application protection. Taking into consideration the review, an opinion is presented regarding which technology is superior and in what circumstances.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".