Design and Implementation of Distributed Web Application Vulnerability Assessment Tools for Securing Complex Microservices Environment
Bibliographic record
Abstract
Modern web applications with complex distributed architectures present significant challenges in vulnerability assessment that traditional approaches fail to address effectively.This research introduces the Distributed Vulnerability Management System (DVMS), implementing a multi-agent architecture to enhance vulnerability detection while eliminating single points of failure.The methodology employs the Nuclei vulnerability scanner across five Open Web Application Security Project (OWASP) security domains, expanding beyond conventional vulnerabilities to include Security Misconfiguration, Vulnerable Components, and Sensitive Data Exposure.Experimental results demonstrate detection accuracies of 80% for Injection, 85.71% for XSS, 80% for Security Misconfiguration, 50% for Vulnerable Components, and 90.91% for Sensitive Data Exposure.The distributed architecture enables parallel processing and optimizes security resource allocation across network infrastructures.While showing promising results in comprehensive security coverage, the system identifies areas for future enhancement in detection accuracy and vulnerability scope expansion.This research contributes a scalable, distributed approach to vulnerability management particularly suited for modern web applications, providing organizations with enhanced security assessment capabilities in complex technological environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".