Fail-fast malware signature validation via parallel downsampling
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Solution Overview
Problem
Traditional malware detection methods, particularly signature matching, face inefficiencies due to the computational expense of comparing object signatures against large databases of known clean objects, leading to high resource utilization and prolonged processing times.
Innovation Solution
A system and method that downsample the database of known clean signatures using probabilistic approaches and machine learning-based clustering algorithms to reduce the search space, allowing for parallel scanning and early termination of scanner instances once a threshold of matches is exceeded, thereby implementing a fast-fail model for signature validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional signature matching is used to validate malware classification, then accuracy is improved, but processing time and resource utilization increase significantly
Solution Approach 1:
The clean object signatures database is divided into multiple partitions or subsets. Instead of comparing the object signature against the entire database sequentially, the validation process segments the search space into manageable parts that can be processed in parallel, significantly reducing the time required while maintaining comprehensive coverage of the database.
Solution Approach 2:
The system performs preliminary downsampling of the clean object signatures database to create a reduced representation that retains the essential characteristics needed for validation. This pre-processed subset enables faster initial filtering and comparison operations, with subsequent verification only performed on promising candidates rather than the entire database.
Solution Approach 3:
The system performs a threshold-based validation where it stops the comparison process once a sufficient number of matches or non-matches are found to confidently determine the classification. This partial action approach does not require exhaustive comparison of all database entries, yet provides statistically significant validation results that maintain high reliability.
2Reliability
If the entire clean object signatures database is scanned to validate classification, then false-positive rate is reduced, but resource utilization increases
Solution Approach 1:
The system performs preliminary downsampling and clustering on the clean object signatures database to create a condensed representation that preserves the distribution and characteristics of the full dataset. This pre-processed structure enables efficient validation with reduced computational resources while maintaining the ability to detect false positives at acceptable rates.
Solution Approach 2:
The validation process segments the database into multiple partitions that can be processed independently and in parallel. This segmentation reduces the memory and computational requirements for any single validation operation, allowing the system to maintain low resource utilization while still providing comprehensive validation coverage across the entire database.
3Productivity
If signature database is downsampled to reduce search space, then processing speed is improved, but measurement precision may deteriorate
Solution Approach 1:
The system transforms the signature database into different representational forms through clustering and downsampling operations. By changing the parameters of representation (from individual signatures to clustered groups or probabilistic distributions), the system maintains the essential matching characteristics while reducing the data volume, thereby preserving accuracy while improving speed.
Solution Approach 2:
The system creates a simplified copy or representation of the full signature database that captures the essential patterns and distributions. This downsampled copy is used for initial validation operations, and it is constructed to preserve the statistical properties and matching behavior of the complete database, ensuring that precision is maintained despite the reduced size.
Data Source
AI summary
A computer-implemented method of updating a malware signature data structure includes classifying an object under analysis as malicious, and computing an object signature of the object under analysis; and validating the classification, including dividing a clean object signatures data structure into a plurality of subunits; assigning the subunits to dedicated data structures; scanning the dedicated data structures, in parallel, with a plurality of scanner instances; and upon at least one scanner instance determining, above a threshold, that the object signature for the object under analysis matches the clean objects signatures data structure, rejecting the object signature for inclusion in the malware signature data structures and terminating the scanner instances.