False Positive Reduction
Investigates how intelligent validation can reduce noisy scanner results.
Cybersecurity Research Project
VERA
Vulnerability Evidence Reasoning Architecture — a research project focused on reducing false positives in automated vulnerability detection through evidence correlation, machine learning and continuous learning.
Evidence Classification
Uses machine learning ideas to classify evidence and estimate finding confidence.
Target Vulnerabilities
Focuses on XSS, SSTI, SQL Injection, IDOR and RCE.
Research Design
Currently in architecture definition and research planning phase.
Overview
What is VERA?
VERA is a research project focused on improving the reliability of automated vulnerability detection systems by analyzing multiple sources of evidence before classifying a finding as valid.
Instead of relying only on payload matching or signature-based detection, VERA evaluates positive evidence, negative evidence, contextual information, application behavior, technology fingerprints, payload history and human feedback.
Research Motivation
Why this research matters
Modern security scanners can discover thousands of potential vulnerabilities, but many of these findings require manual validation because they may be false positives.
This problem affects vulnerability assessment platforms, bug bounty programs, security teams and small organizations with limited security resources.
VERA aims to address this challenge by introducing an intelligent evidence correlation architecture capable of learning from previous observations and improving validation accuracy over time.
Research Question
Main investigation
Can evidence correlation and machine learning reduce false positives?
The central research question is whether evidence correlation and machine learning can reduce false positives in automated vulnerability detection systems without significantly reducing detection capability.
Objectives
What VERA aims to achieve
Reduce false positives in automated vulnerability detection.
Improve confidence estimation for vulnerability findings.
Learn from historical payload performance.
Analyze the influence of Web Application Firewalls.
Use human-assisted validation to improve future decisions.
Provide explainable security decisions.
Evaluate deep learning for evidence classification.
Target Vulnerabilities
Initial research scope
Cross-Site Scripting
Server-Side Template Injection
SQL Injection
IDOR
Remote Code Execution
Core Components
Architecture modules
Evidence Engine
Collects and analyzes security evidence generated during testing.
Payload Intelligence
Tracks payload performance, execution probability, false positive rates and contextual behavior.
WAF Tracker
Observes how defensive technologies influence payload execution and validation results.
Encoding Strategy Engine
Studies how payload encodings affect detection accuracy, execution behavior and false positive generation.
Deep Learning Classifier
Uses transformer-based models to classify evidence as positive, negative or neutral.
Human Feedback Learning
Incorporates analyst validation to continuously improve future decisions.
Explainable Confidence Engine
Provides transparent reasoning behind each classification and confidence score.
Technical Stack
Technologies and concepts
Python
Machine Learning
Deep Learning
Transformers
Evidence Correlation
Cybersecurity
Vulnerability Validation
Human Feedback
Explainable AI
Architecture
System organization
VERA/
├── Evidence Engine
│ ├── Positive Evidence
│ ├── Negative Evidence
│ └── Neutral Evidence
├── Payload Intelligence
│ ├── Payload History
│ ├── Execution Probability
│ └── False Positive Rate
├── WAF Tracker
├── Encoding Strategy Engine
├── Deep Learning Evidence Classifier
├── Human Feedback Learning
├── Explainable Confidence Engine
└── Experimental EvaluationExpected Contributions
Research contribution
VERA investigates a new approach for automated vulnerability validation by combining evidence correlation, machine learning, deep learning, human feedback and continuous payload intelligence.
The expected outcome is a measurable reduction in false positives while maintaining strong detection capability.
Research Roadmap
Project phases
Research design and architecture definition.
Laboratory development for controlled vulnerability testing.
Dataset generation from positive and negative evidence.
Model training and evidence classification experiments.
Experimental evaluation and comparison with baseline scanners.
Scientific article writing and publication.
Follow VERA's development
VERA is under active research design, with future phases focused on laboratory development, dataset generation, model training, experimental evaluation and scientific publication.
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