An Experimental Study on Detecting and Mitigating Vulnerabilities in Web Applications
Bibliographic record
Abstract
The increasing use of the internet has led to a growing number of security threats.Computers, smartphones, smartwatches, and other mobile devices associated with the internet face different threats and exploits.In those cases, different services are provided through web applications only.Those applications are vulnerable to hacking.There are over 1.9 billion websites today, and everything is connected to the network.According to the new national vulnerability database update, 10,683 weaknesses were found in web applications in the first quarter of 2023.The websites have the most significant details of the clients, like personal details, financial details, and so on.Checking all the web application weaknesses is not a silver bullet.So, vulnerability scanners play a significant role in web application security.Vulnerability analysis and penetration testing are two distinct vulnerability types of testing.These tests can help identify all the vulnerabilities in a web application, even those not detected by vulnerability scanners.While certain users access this vulnerability analysis data with just honest goals, like creating some security measures to avoid those vulnerabilities, some utilize it to recognize ways of destroying significant information and records of websites.As it is notable, the term penetration testing is also ethical hacking.The current paper aims to investigate penetration testing on web applications.The paper discusses the different types of penetration testing, the tools and techniques used, and the benefits of penetration testing.It also suggests the challenges of penetration testing and the steps that can be taken to mitigate these challenges.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".