Hybrid Malware Detection via Static and Dynamic Analysis
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Solution Overview
Problem
Current malware detection tools are limited by the lack of integration between static and behavior analysis, leading to reduced effectiveness in identifying malicious files and processes, as each approach has distinct strengths but lacks depth when used alone.
Innovation Solution
The integration of metadata from static analyzers with behavioral tools, using machine-learning models to classify files based on both static and dynamic features extracted before and during execution, enhancing the accuracy of malware detection by combining static Portable Executable metadata with stack traces and API calls sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If only static analysis is used for malware detection, then the depth of file analysis is improved, but the ability to detect malware at runtime is worsened
Solution Approach 1:
The patent combines static analysis and dynamic analysis into a unified malware detection system. The hybrid architecture integrates the depth of static file analysis with the runtime detection capabilities of dynamic behavior analysis, allowing the system to leverage both approaches simultaneously rather than relying on one method alone.
Solution Approach 2:
The detection system is designed to perform multiple functions: it can conduct static analysis of file characteristics, execute dynamic analysis of runtime behavior, and combine both approaches for comprehensive malware detection. This multi-functional capability allows the system to adapt to different detection needs and scenarios.
2Reliability
If only dynamic analysis is used for malware detection, then the ability to detect malware at runtime is improved, but the depth of static file analysis is worsened
Solution Approach 1:
The system merges dynamic analysis capabilities with static analysis components, creating a hybrid detection mechanism that preserves the runtime detection strength of dynamic analysis while incorporating the deep file analysis capabilities of static analysis through integrated feature extraction and classification modules.
Solution Approach 2:
The system performs preliminary static analysis to extract file characteristics before runtime dynamic analysis. This preliminary action allows the system to prepare classification models and feature sets in advance, enhancing the depth of analysis without compromising runtime detection capabilities.
3Device complexity
If static analysis and behavior analysis are used as separate approaches, then the simplicity of each individual tool is maintained, but the overall effectiveness of malware detection is worsened
Solution Approach 1:
The patent implements a unified architecture that merges static analysis and behavior analysis components into a single integrated system. This integration maintains the functional simplicity of individual analysis tools while achieving enhanced detection effectiveness through coordinated operation of both approaches within one system framework.
Data Source
AI summary
A system and method for malware detection uses static and dynamic analysis to augment a machine learning model. At the training step, static and dynamic features are extracted from training datasets and used to train a malware classification model. The malware classification model is used to classify unknown files based on verdicts from both static and dynamic models.