API Extraction for Software Piracy Detection
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Solution Overview
Problem
Dynamic analysis for identifying software piracy is time-consuming due to the large amount of data that needs to be analyzed, which hinders efficient detection and identification.
Innovation Solution
A data processing method that extracts API names from software programs, converts them into unique text sequences, establishes a frequency table, records these sequences in a log file, and compresses the data using schemes like Repeated Call Compression, Repeated Call Pattern Compression, or Pattern-set Replacement Compression to reduce the data volume for similarity comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic analysis is used to identify software piracy, then identification accuracy is improved, but analysis time increases significantly
Solution Approach 1:
The patent extracts only the essential API call information from the complete execution data, separating the critical identification features from the large volume of redundant execution data. This extraction process maintains identification accuracy while reducing the data volume requiring analysis.
Solution Approach 2:
The patent segments the analysis process into distinct stages: data collection, API extraction, frequency table generation, and similarity comparison. By dividing the workflow, the system processes only necessary information at each stage, reducing overall analysis time while preserving accuracy.
2Measurement precision
If complete execution data is analyzed for software piracy identification, then identification accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential API call information from the complete execution data, separating the critical identification features from the large volume of redundant execution data. This extraction process maintains identification accuracy while reducing the data volume requiring analysis.
Solution Approach 2:
The patent transforms the raw execution data into frequency tables that represent API call patterns. By changing the data representation from detailed execution traces to aggregated frequency statistics, the system reduces processing complexity while preserving the essential characteristics needed for accurate piracy identification.
3Measurement precision
If API call data is extracted and recorded in detail, then similarity comparison accuracy is improved, but data volume increases
Solution Approach 1:
The patent transforms the raw execution data into frequency tables that represent API call patterns. By changing the data representation from detailed execution traces to aggregated frequency statistics, the system reduces processing complexity while preserving the essential characteristics needed for accurate piracy identification.
Solution Approach 2:
The patent combines multiple API call occurrences into frequency counts, merging redundant information into compact representations. This consolidation maintains the essential pattern information for accurate similarity comparison while dramatically reducing the total data volume that must be processed and stored.
Data Source
AI summary
A data processing method, apparatus, and computer program product for performing a similarity comparison between software programs are provided. An aspect of the invention provides a data processing method for performing a similarity comparison between two programs, where the data processing method includes: (a) extracting API names from each program and converting the API names to unique text sequences of a particular byte; (b) establishing a frequency table by counting a frequency of the extracted API names; (c) recording the API names converted into the unique text sequences in a log file in the order of extraction; and (d) saving the frequency table as a file and compressing the log file.