File Mutation System Using Section Information and Rules
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Solution Overview
Problem
Current file mutation methods for software vulnerability testing, such as file fuzzing, face challenges in generating meaningful test cases efficiently due to limitations in handling various file formats and high false positives, often relying on randomness which wastes computing resources and results in inefficient testing.
Innovation Solution
A file mutation system utilizing file section information and mutation rules to differentiate data types within a file, allowing for targeted mutations based on specific data types, thereby generating more accurate and efficient test cases by applying appropriate mutation methods to each section of a file.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If random data is used for file mutation, then test case generation is simple, but test efficiency decreases and computing resources are wasted
Solution Approach 1:
The patent segments the file into multiple sections (e.g., header, data, trailer) and applies different mutation strategies to each section based on its specific characteristics. This allows targeted mutations that preserve file validity while introducing meaningful variations, thereby improving test efficiency without sacrificing generation simplicity.
Solution Approach 2:
The patent applies local quality by differentiating mutation methods according to the data type and location within the file. Different sections receive tailored mutation operations (e.g., bit flipping for binary data, string insertion for text sections), ensuring that each part of the file is mutated appropriately for its function, thus reducing false positives and improving overall test efficiency.
2Reliability
If mass test cases are created to ensure coverage, then testing completeness improves, but computing resource consumption increases
Solution Approach 1:
The patent implements dynamic test case generation by adaptively selecting mutation strategies based on the analyzed file structure and identified vulnerability patterns. The system dynamically adjusts the number and type of test cases generated according to the specific file format and detected issues, ensuring comprehensive coverage while minimizing unnecessary computation resources.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system analyzes the results of mutation operations and adjusts subsequent test case generation accordingly. By learning from previous test outcomes and file analysis, the system optimizes the test case selection process to achieve comprehensive coverage with fewer test cases, thereby reducing computing resource consumption.
3Device complexity
If file mutation does not consider data correlations, then mutation process is simpler, but false positives increase
Solution Approach 1:
The patent performs preliminary analysis of the file structure, data types, and correlations before applying mutations. By pre-analyzing the file to understand the relationships between different sections and data elements, the system can generate mutations that respect these correlations, thereby reducing false positives without significantly increasing the overall mutation process complexity.
Data Source
AI summary
Provided are a file mutation method and a system using file section information and mutation rules. The file mutation system includes: a file section information extraction module obtaining file section information with respect to a sample file of a known file format; a file section information production module producing file section information with respect to a sample file of an unknown format; a mutation rule production module receiving a user input that a mutation rule is applied and producing a mutation rule, the mutation rule defining a mutation function that is to be applied to each data type; and a file mutation module receiving the sample file and producing a plurality of test case files that are created by mutating the sample file through the file section information processed in the file section information extraction module and the file section information production module and the mutation rule from the mutation rule production module.