Binary File Risk Scoring Using Security Complexity Metrics
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Solution Overview
Problem
Existing systems face challenges in quickly and efficiently identifying high-risk areas within large software components due to their volume and complexity, making it difficult to prioritize security vulnerabilities effectively.
Innovation Solution
A computer system analyzes binary files by evaluating characteristics such as code size, data size, number of insecure APIs, conditional statements, and cyclomatic complexity to generate a risk assessment file that quantifies security vulnerabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary files are analyzed in detail to identify security vulnerabilities, then measurement precision of security risk assessment is improved, but analysis time and processing complexity increase
Solution Approach 1:
The patent segments the binary analysis process into distinct characteristics (code size, data size, number of insecure APIs, conditional statements, cyclomatic complexity) and evaluates them independently. This segmentation allows the system to assess multiple security dimensions simultaneously without requiring exhaustive analysis of every code instruction, thereby improving measurement precision while controlling analysis time.
Solution Approach 2:
The patent performs preliminary evaluation by extracting and analyzing key characteristics from binary files before conducting deeper security assessments. By pre-evaluating metrics such as code size, data size, and cyclomatic complexity, the system can quickly identify high-risk areas and prioritize further investigation, reducing overall analysis time while maintaining accurate risk assessment.
2Reliability
If comprehensive security analysis is performed on large software components, then reliability of security assessment is improved, but device complexity and processing requirements increase
Solution Approach 1:
The patent applies local quality by focusing analysis on specific high-risk characteristics within binary files rather than uniformly analyzing all code elements. By prioritizing metrics such as the number of insecure APIs, cyclomatic complexity, and data size, the system achieves reliable security assessment of critical areas without requiring complex processing of entire software components, thus reducing overall processing complexity.
Solution Approach 2:
The patent transforms the security assessment process by changing parameters from traditional detailed code analysis to metric-based evaluation. By converting binary file characteristics into quantifiable parameters (code size, data size, number of conditions, cyclomatic complexity), the system achieves reliable assessment with reduced processing complexity, as these parameters can be extracted and evaluated more efficiently than full code analysis.
3Productivity
If multiple security metrics are evaluated simultaneously, then productivity of risk identification is improved, but measurement precision requirements increase
Solution Approach 1:
The patent implements multi-functionality by using a unified evaluation framework that simultaneously assesses multiple security metrics (code size, data size, number of insecure APIs, conditional statements, cyclomatic complexity). This universal approach allows the system to evaluate different aspects of security risk through a consistent set of measurements, improving productivity without requiring excessively precise individual metric measurements, as the combined assessment provides comprehensive risk insight.
Data Source
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AI summary
A computer includes a memory and a processor programmed to execute instructions stored in the memory. The instructions include processing a binary file to determine a security risk associated with the binary file. Processing the binary file includes determining a characteristic from metadata of the binary file. The security risk associated with the binary file is based at least in part on the characteristic of the binary file.