Software Hash Section Search Using Random-Data Segmentation
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Solution Overview
Problem
Existing methods for searching software data for hash sections, particularly password hashes, are inefficient due to high resource requirements and memory overhead, especially when dealing with large binary files.
Innovation Solution
A computer-implemented method that selects data segments based on a random criterion to identify hash sections, reducing the amount of data to be searched by prioritizing randomly distributed data, thereby lowering memory requirements and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire software data is searched for hash sections using traditional methods, then all hash sections can be detected, but memory requirements and processing time increase significantly
Solution Approach 1:
The patent divides the software data into multiple data sections and applies different search strategies to different segments. Random-criterion-based sections are searched with full hashing, while non-random sections are skipped, thereby segmenting the search effort to reduce memory requirements while maintaining detection coverage.
Solution Approach 2:
The patent extracts and identifies sections of data that satisfy the random criterion using statistical analysis, then extracts hash sections from only those identified regions. This extraction approach isolates the relevant search areas from the entire software data, reducing the quantity of data processed in memory.
2Reliability
If the entire software data is searched for hash sections using traditional methods, then all hash sections can be detected, but processing time increases significantly
Solution Approach 1:
The patent segments the software data based on randomness criteria and applies selective searching only to segments that satisfy the random criterion. This segmentation avoids wasting processing time on non-random sections that are unlikely to contain hash sections, thereby reducing overall processing time while maintaining detection reliability.
Solution Approach 2:
The patent applies partial action by searching only a portion of the software data (those sections satisfying the random criterion) rather than the entire dataset. This partial searching approach is sufficient to detect hash sections with high probability while significantly reducing processing time compared to exhaustive searching.
3Quantity of substance
If a random criterion is used to select data sections for searching, then memory requirements are reduced, but the complexity of the search method increases
Solution Approach 1:
The patent changes the parameter of data selection from exhaustive to selective based on randomness criteria. By introducing statistical parameters (randomness evaluation) as the selection criterion, the method reduces memory requirements while the added complexity is offset by the efficiency gains from processing fewer data sections.
Data Source
Figure 1
AI summary
The computer-implemented method is used to search software data for hash sections. In the method, data sections are selected from the software data that satisfy a random criterion that distinguishes randomly distributed data from non-randomly distributed data, and the method predominantly searches the selected data sections for the hash sections. The computer program product contains program instructions that cause a computer to execute a method according to one of the preceding claims when the program instructions are executed on the computer.