Software Vulnerability Detection via Validation Machine Analysis
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Solution Overview
Problem
Verifying software for malicious code, vulnerabilities, or backdoors is a difficult task, as existing methods cannot automatically detect all issues, requiring human inspection for suspicious sections.
Innovation Solution
Instrumenting a validation machine with tools and monitors to capture static and dynamic software behavior, logging data to detect malicious code, and using machine learning to neutralize or flag potentially malicious activities, with human inspection for unautomatable actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated analysis methods are used to detect malicious code, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system segments the detection process into multiple specialized components: static analysis tools examine code structure and patterns, dynamic analysis monitors runtime behavior, data mining identifies suspicious patterns, and machine learning classifiers evaluate overall risk. This segmentation allows each component to focus on specific aspects, improving both automation and precision.
Solution Approach 2:
The system implements feedback loops where detection results are continuously refined. Human inspection of flagged suspicious sections provides feedback that trains the machine learning models, improving their precision over time. The system also uses feedback from confirmed malicious code to update detection rules and patterns, enhancing automated detection accuracy.
2Measurement precision
If comprehensive monitoring tools are deployed to capture all software behavior, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
The validation machine is designed as a multi-functional system that performs diverse analysis tasks through integrated tools. A single platform executes static analysis, dynamic monitoring, data mining, and machine learning evaluation, reducing overall system complexity while maintaining comprehensive detection capabilities through shared infrastructure and coordinated toolsets.
3Measurement precision
If human inspection is used for suspicious code sections, then measurement precision is improved, but productivity deteriorates
Solution Approach 1:
The system applies partial automation by using machine learning and pattern recognition to handle the majority of code analysis automatically. Human inspection is reserved only for suspicious sections that require expert judgment, rather than requiring human review of all code. This partial action approach maintains high precision for critical cases while preserving productivity through automated handling of routine analysis.
4Reliability
If multiple analysis tools are integrated into the validation machine, then reliability is improved, but device complexity worsens
Solution Approach 1:
The system merges multiple analysis tools into a unified validation machine platform. Different analysis components (static analyzers, dynamic monitors, data mining engines, and machine learning classifiers) are integrated and coordinated to work together synergistically. This merging improves reliability through cross-validation and comprehensive analysis while managing complexity through unified architecture and centralized control.
Data Source
AI summary
A system and method determines whether software includes malicious code. A validation machine is instrumented with tools and monitors that capture the static and dynamic behavior of software. Software under examination is executed on the validation machine, and the tools and monitors are used to log data representative of the behavior of the software to detect vulnerable or malicious code. If possible, one or more operations are automatically performed on the software to enhance the security of the software by neutralizing the vulnerable or malicious code. Activities that cannot be neutralized automatically are flagged for human inspection. The software executed on the validation machine may be source code or non-source code, with different operations being disclosed and described in each case.