Multistage Malware Analysis for Obfuscated Content Detection
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Solution Overview
Problem
Conventional malware detection systems fail to efficiently detect malware in obfuscated content, leading to high false negatives and substantial computing resource demands.
Innovation Solution
A multistage malware analysis scheme that de-obfuscates content using an emulator and then statically analyzes it, followed by dynamic analysis in a virtual runtime environment, to efficiently detect malware while minimizing false positives and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional malware detection systems analyze obfuscated content, then detection coverage is improved, but false negatives increase and computing resource consumption increases
Solution Approach 1:
The malware detection system is divided into multiple analysis stages: initial static analysis, de-obfuscation analysis, and dynamic analysis. Each stage processes only suspicious content identified by the previous stage, segmenting the overall detection task to reduce unnecessary computing resource consumption while maintaining comprehensive detection coverage
Solution Approach 2:
The system performs preliminary static analysis and de-obfuscation analysis before conducting resource-intensive dynamic analysis. This preliminary filtering identifies and isolates only the most suspicious content, allowing the system to prepare for dynamic analysis in advance and avoid wasting resources on benign content
2Measurement precision
If dynamic analysis is performed on all suspicious content, then detection accuracy is improved, but computing resource demands increase substantially
Solution Approach 1:
Different analysis methods are applied to different content based on their suspiciousness level and characteristics. Low-risk suspicious content undergoes lighter analysis while high-risk content receives full dynamic analysis, optimizing resource allocation to match the actual detection needs of each content item
Solution Approach 2:
The system uses feedback from static analysis and de-obfuscation analysis results to dynamically adjust the scope and intensity of subsequent dynamic analysis. Content that shows strong malicious indicators triggers full dynamic analysis, while content with weaker indicators may receive reduced analysis, creating a feedback-driven resource optimization mechanism
Data Source
AI summary
A malware detection system configured to detect suspiciousness in obfuscated content. A multi-stage static detection logic is utilized to detect obfuscation, make the obfuscated content accessible, identify suspiciousness in the accessible content and filter non-suspicious non-obfuscated content from further analysis. The multi-stage static detection logic includes a controller, a de-constructor, and a post-processor. The controller is configured to receive content while the de-constructor configured to receive content from the controller and deconstruct the content using the analysis technique selected by the controller. The post-processor is configured to receive the de-constructed content from the de-constructor, determine whether a specimen within the de-constructed content is suspicious, and remove non-suspicious content from further analysis.