Crowdsourced Vulnerability Validation via Automated Replica Scoring
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Solution Overview
Problem
Current crowdsourcing-based vulnerability detection systems face challenges in automatically validating submissions, leading to high signal-to-noise ratios and increased costs due to reliance on human intervention and inefficient validation processes.
Innovation Solution
A method and system for automatically validating vulnerabilities in a crowdsourcing environment, involving identifying vulnerabilities, preprocessing data, generating structured data, creating a replica of the vulnerability, calculating a confidence score, and executing validating instructions based on the score, which reduces the need for manual triage and increases validation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a crowdsourcing based vulnerability detection system uses a freelance workforce strategy of 'spray and pray' to increase continuous coverage, then the quantity of vulnerability submissions increases, but the signal-to-noise ratio of valid to invalid submissions substantially increases (worsens)
Solution Approach 1:
The patent introduces an automated validation system as an intermediary between freelance testers and the organization. This system uses machine learning models and validation rules to filter and assess vulnerability submissions automatically, reducing the need for manual review while maintaining high accuracy in distinguishing valid from invalid submissions.
Solution Approach 2:
The validation system enables submissions to self-assess through automated scoring and validation rules. Each vulnerability submission is automatically evaluated against predefined criteria and machine learning models, allowing the system to self-filter without human intervention for routine cases.
2Measurement precision
If a crowdsourcing based vulnerability detection system relies on human intervention during the vulnerability triage process for validation, then the quality of validation improves, but the total cost of vulnerability detection substantially increases
Solution Approach 1:
The patent replaces the mechanical human review process with an automated electronic validation system. Machine learning models, validation rules, and scoring algorithms automatically assess vulnerability submissions, eliminating the need for human triage personnel while maintaining or improving validation accuracy.
Solution Approach 2:
The validation system performs self-assessment of vulnerability submissions through automated scoring and validation against predefined criteria. The system independently evaluates submissions without requiring human energy input, reducing operational costs while maintaining validation quality.
3Productivity
If a crowdsourcing based vulnerability detection system processes a high volume of low value submissions, then the continuous coverage improves, but the time and energy consumed by the organization in reviewing submissions increases
Solution Approach 1:
The automated validation system acts as an intermediary that rapidly processes and filters high volumes of submissions before they reach human reviewers. Machine learning models and validation rules quickly assess each submission, allowing the organization to maintain continuous coverage while minimizing the time spent on manual review of low-value submissions.
Solution Approach 2:
The validation system performs self-service processing of vulnerability submissions through automated evaluation and scoring. This eliminates the need for organization personnel to spend time reviewing low-value submissions, as the system independently filters and prioritizes them based on predefined criteria and machine learning assessments.
4Measurement precision
If a crowdsourcing based vulnerability detection system defines a business model that only supports vetted testers or moves to private and invite only projects to address the signal-to-noise problem, then the quality of submissions improves, but the core promise of leveraging the law of large numbers by engaging a freelance community is defeated
Solution Approach 1:
The automated validation system serves as an intermediary that enables the organization to maintain an open freelance community while ensuring submission quality. The system automatically filters and validates submissions from all testers regardless of vetting status, allowing broad community engagement while maintaining high quality standards through machine learning and validation rules.
Solution Approach 2:
The validation system enables self-service quality assurance for all testers in the freelance community. Each submission is automatically evaluated against predefined criteria and machine learning models, allowing unvetted testers to participate while maintaining submission quality through automated assessment rather than requiring selective invitation or vetting processes.
Data Source
AI summary
A method for validating a vulnerability submitted by a tester in a crowdsourcing environment. The method comprises identifying at least one vulnerability within at least one computer resource and receiving vulnerability data corresponding to the at least one vulnerability. The method further comprises pre-processing the vulnerability data to generate structured data and generating a replica of the vulnerability using the structured data and at least one validator. Further, the method comprises calculating a confidence score of the vulnerability using the replica of the vulnerability and a result of the at least one validator. The method executes at least one validating instruction based on the confidence score of the vulnerability.