Malware Clustering via Execution Behavior Vectorization
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Solution Overview
Problem
The proliferation of malware variants poses a significant challenge for detection and clustering due to their rapid evolution and obfuscation techniques, leading to inefficiencies in existing clustering methods that rely on analyzing malware samples themselves, which are computationally expensive and cumbersome.
Innovation Solution
The approach involves clustering malware samples based on their execution behavior reports generated in a controlled environment, using feature selection and vectorization to generate similarity values, and analyzing function call graphs to identify and group malware by functional behavior rather than syntax, thereby improving speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If malware clustering is performed by analyzing malware samples themselves, then detection accuracy may be maintained, but computational cost and processing time increase significantly
Solution Approach 1:
The patent performs preliminary analysis by executing malware samples in a controlled environment before clustering to generate execution behavior reports. This preliminary action captures essential behavioral characteristics in advance, allowing subsequent clustering operations to work with pre-processed data rather than raw malware samples, thereby reducing computational cost and processing time while maintaining detection accuracy
Solution Approach 2:
The patent extracts specific execution behavior features from complete malware execution reports to create condensed feature sets. By taking out only the most relevant behavioral characteristics (such as system calls, file operations, network activities) and excluding redundant information, the clustering process operates on streamlined data that maintains detection precision while significantly reducing computational overhead
2Reliability
If malware clustering analyzes complete execution behavior reports, then comprehensive behavioral analysis is achieved, but data processing complexity and computational resources increase
Solution Approach 1:
The patent extracts a reduced feature set containing only the most discriminative execution behavior characteristics from complete execution reports. This extraction process identifies and isolates key behavioral features (such as critical system calls, essential file operations, significant network activities) while removing redundant or less informative data, thereby maintaining reliable behavioral analysis with reduced processing complexity
Solution Approach 2:
The patent applies different processing strategies to different portions of execution behavior data based on their importance. Critical behavioral features receive detailed analysis and are retained in the reduced feature set, while less significant aspects are summarized or excluded. This local quality approach ensures comprehensive analysis of essential behaviors while reducing overall data processing complexity
3Productivity
If traditional clustering methods are used on malware samples, then clustering can be performed, but the methods are cumbersome and inefficient given the rapid evolution of malware variants
Solution Approach 1:
The patent changes the fundamental parameters used for clustering from static malware sample characteristics to dynamic execution behavior features. By transitioning from analyzing fixed malware code properties to measuring variable behavioral parameters (system calls, file operations, network activities) that capture malware's actual runtime behavior, the system achieves both high clustering speed and strong adaptability to evolving malware variants
Solution Approach 2:
The patent performs preliminary execution of malware samples to generate behavior reports before clustering. This preliminary action captures the dynamic behavioral characteristics of malware in advance, creating a standardized behavioral profile that enables rapid clustering operations. The pre-captured behavioral data allows the system to quickly adapt to new malware variants without requiring complex real-time analysis during clustering
Data Source
AI summary
Techniques are disclosed relating to malware clustering based on execution-behavior reports. In some embodiments, a computer system may access malware information that includes a plurality of reports corresponding to a plurality of malware samples. In some embodiments, each of the malware reports specifies a set of features relating to execution behavior of a corresponding malware sample. The computer system may, in various embodiments, process the plurality of reports to generate a plurality of vectors that includes, for each of the malware samples, a corresponding vector indicative of the execution behavior of the corresponding malware sample. Based on the plurality of vectors, the computer system may generate similarity values indicative of a similarity between ones of the plurality of vectors. Further, based on the similarity values, the computer system may assign each of the plurality of malware samples to one of a plurality of clusters of related malware samples.