Automated IOC Creation via Command Line Argument Clustering
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Solution Overview
Problem
Existing malware detection methods struggle to effectively identify polymorphic malware without sandboxing analysis, as they rely on static characteristics and behavioral mimicry, making it difficult to distinguish between benign and malicious activities.
Innovation Solution
The automated creation of lightweight behavioral indicators of compromise (IOCs) based on command line arguments, which clusters execution records to flag suspicious patterns, allowing for dynamic malware detection without the need for sandboxing, even for polymorphic malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static characteristics and behavioral mimicry are used for malware detection, then detection coverage is improved, but false positives increase and polymorphic malware cannot be effectively identified
Solution Approach 1:
The patent transitions from static malware detection to dynamic behavioral analysis by monitoring command-line arguments in real-time during binary execution. This dynamic approach captures the actual runtime behavior of programs, enabling effective identification of polymorphic malware that changes its static characteristics while maintaining consistent behavioral patterns.
Solution Approach 2:
The patent replaces traditional mechanical/static analysis methods with information-based behavioral monitoring. Instead of analyzing fixed binary characteristics, the system substitutes static file analysis with dynamic observation of command-line arguments and execution patterns, fundamentally changing the detection mechanism from structural to behavioral.
2Reliability
If sandboxing analysis is used for malware detection, then polymorphic malware identification is improved, but system complexity and resource consumption increase
Solution Approach 1:
The patent extracts the essential detection function from complex sandboxing environments and implements it directly on live hosts through lightweight command-line argument monitoring. By taking out only the critical behavioral observation capability and applying it in the production environment, the system achieves polymorphic malware detection without requiring isolated sandbox infrastructure.
Solution Approach 2:
The patent employs lightweight, disposable monitoring mechanisms that track command-line arguments without requiring persistent sandbox environments. This approach uses minimal resources compared to traditional sandboxing, enabling broad deployment across multiple hosts simultaneously while maintaining effective polymorphic malware detection.
3Measurement precision
If behavioral monitoring is implemented on live hosts, then detection precision is improved, but false positives may increase from legitimate applications
Solution Approach 1:
The patent performs preliminary clustering and baseline establishment of command-line argument patterns before actual malware detection begins. By pre-processing and categorizing normal behavioral patterns, the system creates a reference framework that enables more accurate distinction between legitimate applications and malware, reducing false positives while maintaining high detection precision.
Solution Approach 2:
The patent implements feedback mechanisms that continuously refine detection accuracy by analyzing clustered command-line argument patterns. The system uses observed behavioral patterns to improve its detection capabilities over time, learning from both legitimate and malicious behaviors to reduce false positives while maintaining high precision in identifying actual threats.
Data Source
AI summary
In one embodiment, a device obtains execution records regarding executions of a plurality of binaries. The execution records comprise command line arguments used during the execution. The device determines measures of similarity between the executions of the binaries based on their command line arguments. The device clusters the executions into clusters based on the determined measures of similarity. The device flags the command line arguments for a particular one of the clusters as an indicator of compromise for malware, based on at least one of the binaries associated with the particular cluster being malware.