Security Scan Classifier for Auditor Preference Learning
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Solution Overview
Problem
Current application security scanning engines face challenges in accurately prioritizing vulnerabilities due to imprecision in analysis and contextual awareness, leading to potential missed exploitable threats and inefficient human auditing processes.
Innovation Solution
Implementing machine-based classifiers trained on historical human-audited data to prioritize application security scan results, distinguishing between in-scope and out-of-scope issues and reassigning priority levels, thereby reducing the need for thorough scans and enhancing the efficiency of human expert analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If thorough security scans are performed to identify all vulnerabilities, then the reliability of vulnerability detection is improved, but the time consumption and complexity of analysis increase
Solution Approach 1:
The system performs preliminary actions by training machine classifiers on historical human-audited scan data before actual vulnerability assessment. The classifiers are pre-trained to recognize patterns and prioritize vulnerabilities, enabling rapid initial sorting of scan results without requiring full human auditing of every vulnerability, thus reducing time while maintaining reliability
Solution Approach 2:
Machine classifiers serve as intermediaries between the security scan engine and human auditors. These classifiers process scan results and prioritize vulnerabilities based on learned patterns from historical data, acting as a mediator that filters and ranks issues before human review, reducing the time burden on human experts while maintaining detection accuracy
2Reliability
If comprehensive security scanning is conducted to ensure no exploitable threats are missed, then the reliability of threat detection is improved, but the complexity of result analysis increases
Solution Approach 1:
The analysis process is segmented into distinct stages: automated machine classification of vulnerabilities based on scan data, prioritization of issues by severity and likelihood, and targeted human auditing of high-priority items. This segmentation reduces overall complexity by dividing the comprehensive analysis into manageable, automated steps followed by focused human review
Solution Approach 2:
The system performs self-service through automated machine classifiers that independently analyze scan results and prioritize vulnerabilities without requiring extensive human intervention. The classifiers use historical data to automatically assess and rank threats, reducing analysis complexity while maintaining comprehensive threat detection through systematic automated processing
3Measurement precision
If human experts audit all security scan results to ensure accurate prioritization, then the measurement precision of vulnerability assessment is improved, but the productivity of the security process decreases
Solution Approach 1:
Human experts perform partial auditing by focusing only on the top-priority vulnerabilities identified by machine classifiers rather than reviewing all scan results. This partial action maintains measurement precision for critical threats while significantly improving productivity by reducing the volume of items requiring human review
Solution Approach 2:
The system implements feedback loops where human auditor decisions on prioritized vulnerabilities are fed back into the training data for machine classifiers. This feedback continuously improves the precision of automated classification over time, allowing the system to maintain high accuracy while processing larger volumes of vulnerabilities, thus improving overall productivity
Data Source
AI summary
A technique includes receiving data representing an output of a security scan of an application and an audit of the security scan by a human auditor. The output represents a security issue with the application, which is identified by the security scan, and the audit represents an analysis of the security issue by the human auditor. The technique includes training a security scan classifier to learn a classification preference of the human auditor. Training the security scan classifier includes, processing the data in a processor-based machine to, based at least in part on the output of the security scan and the analysis of the security scan by the human auditor, learn the classification preference of the human auditor to the issue to build a classification model for the issue.