Polymorphic Malware Detection via Self-Updating Filters
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Solution Overview
Problem
Existing antivirus technologies face challenges in detecting polymorphic malware variants due to their ability to generate thousands of unique variations, which are often missed by initial detection routines, leading to a need for continuous updates and revisions.
Innovation Solution
The system automatically updates filters based on static properties of executable files to ensure correct classification and detection of polymorphic malware strains, modifying filters to include new samples and transmitting these updates to client systems for future scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hand-coded initial detection routines are used to filter executable files, then the detection process becomes faster and less computationally expensive, but polymorphic packer engines can generate variants that are miscategorized and require continuous rehabilitation of the detection routines
Solution Approach 1:
The system enables automatic self-updating of detection filters through machine learning. The filtering system automatically learns from misclassified samples and adjusts its own parameters without requiring manual intervention from antivirus engineers, thus maintaining both speed and accuracy over time
Solution Approach 2:
The system implements feedback loops where detection results are continuously analyzed to identify misclassified samples. This feedback is used to automatically retrain and update the filtering models, ensuring that detection accuracy improves while maintaining fast filtering performance
2Reliability
If comprehensive scans including decryption and signature matching are performed on all executable files, then detection accuracy improves, but the computational cost and time consumption increase significantly
Solution Approach 1:
The detection process is segmented into multiple stages: a fast filtering stage that uses lightweight machine learning models to quickly eliminate benign files, and a comprehensive analysis stage that performs decryption and signature matching only on files that pass the initial filter, thus reducing overall computational cost while maintaining accuracy
Solution Approach 2:
The system performs preliminary filtering using static property analysis and machine learning models before executing computationally expensive decryption and signature matching operations. This preliminary action eliminates the majority of benign files, so that comprehensive scans are only performed on a small subset of suspicious files
3Productivity
If static property filters are used to screen executable files, then the scanning process becomes more efficient, but new polymorphic variants with different static properties may be incorrectly excluded
Solution Approach 1:
The filtering system transitions from static, hand-coded rules to dynamic machine learning models that automatically adapt to new polymorphic variants. The models are continuously retrained on new samples, allowing the filters to evolve and maintain effectiveness against emerging threats while preserving scanning efficiency
Solution Approach 2:
The system automatically adjusts filter parameters based on learned patterns from training data. Instead of using fixed static property thresholds, the machine learning models dynamically determine optimal parameter values that adapt to the characteristics of new polymorphic variants, maintaining both efficiency and adaptability
Data Source
AI summary
A computer-implemented method for identifying polymorphic malware may include identifying a sample of a variant within a polymorphic malware strain. The computer-implemented method may also include identifying a set of filters for identifying the polymorphic malware strain. The computer-implemented method may further include determining that the set of filters incorrectly excludes the sample from being identified as within the polymorphic malware strain. The computer-implemented method may additionally include modifying the set of filters to not exclude the sample from being identified as within the polymorphic malware strain. Various other methods, systems, and computer-readable media are also disclosed.