Security Analysis of SQL Injection Attacks on Multimedia and Journal-Services Sites Using Concatenated Input Validation and Parsing Method (CIVP)
Bibliographic record
Abstract
Web applications and databases continue to face grave danger from SQL injection attacks, which can result in unauthorized access, data modification, and system compromise.This report discusses the methods attackers use to exploit SQL injection vulnerabilities and emphasizes the dangers of successful attacks, such as data leaks and system compromise.This research proposes a comprehensive system for detecting SQL injection attacks using concatenated Input Validation and Parsing Method (CIVP).The site used as experimental material is the Multimedia and Journal Services Site.Based on the results of forensic analysis on the Journal Services Site, there were several attacks in cyberspace, including using SQLMAP and Python.The system created has successfully detected SQL injection attacks.Based on the test results, it was found that the use of the method proposed in this study succeeded in making processing time 15.2% more efficient.Experiments carried out with the method proposed in this study succeeded in increasing the attack detection accuracy from 96-97% to 99.5% with a p-value of 0.008446.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".