Malware Classification via Memory Dump and Grayscale Conversion
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Solution Overview
Problem
The exponential growth of malware objects poses a significant challenge for security services providers, as existing anti-malware systems struggle to quickly identify and classify malware before it can cause destructive work, especially with the use of packers that compress and encrypt files, making them difficult to recognize using computer vision techniques.
Innovation Solution
A method involving a sandbox environment where the binary object is loaded into executable memory, decompressed and decrypted, and then a memory dump is performed to create a binary image, which is converted into an 8-bit grayscale image for analysis by a neural network to classify malware effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If packers compress and encrypt malware files, then malware can evade detection by traditional anti-malware systems, but malware classification becomes more difficult and time-consuming
Solution Approach 1:
The system performs preliminary unpacking of malware files in a sandbox environment before classification. By executing the malware in a controlled sandbox, the system automatically decompresses and decrypts packed files, converting them into a standardized format suitable for computer vision analysis. This preliminary action eliminates the need to analyze encrypted formats directly, significantly reducing classification time while maintaining detection accuracy
Solution Approach 2:
The patent introduces a sandbox environment as an intermediary between the malware file and the classification system. The sandbox acts as a mediator that automatically unpacks and standardizes malware files, converting various packed formats into a common executable format. This intermediary layer shields the classification system from format variations, enabling efficient processing without compromising detection reliability
2Adaptability or versatility
If the system analyzes malware in various packed formats directly, then format-specific analysis is required, but the complexity of the analysis system increases
Solution Approach 1:
The sandbox environment provides a universal processing mechanism that handles all malware formats through the same execution-based unpacking approach. Instead of implementing separate analysis pipelines for different packed formats (UPX, PE, ASPack, etc.), the system uses a single multi-functional sandbox that automatically detects and unpacks any format. This universal approach maintains format adaptability while significantly reducing system complexity
Solution Approach 2:
The system changes the state parameter of malware files from encrypted/compressed to unpacked/executable format through sandbox execution. By transforming all input files into a standardized executable state, the system eliminates the need for format-specific analysis parameters and algorithms. This parameter transformation approach maintains versatility in handling diverse formats while simplifying the analysis process to a uniform procedure
3Measurement precision
If traditional anti-malware systems use signature-based detection, then known malware can be identified, but new and packed malware variants cannot be effectively classified
Solution Approach 1:
The patent replaces traditional mechanical signature-matching mechanisms with computer vision-based analysis of unpacked malware. Instead of comparing files against known signatures, the system uses neural networks to visually analyze the structural and behavioral characteristics of unpacked malware in the sandbox. This substitution enables the system to identify both known and unknown malware variants by recognizing patterns in their executed state, significantly improving adaptability to new threats while maintaining precision through multi-feature analysis
Data Source
AI summary
There is disclosed in one example a method of detecting computer malware, including: receiving a binary object for analysis; allocating the binary object to a sandbox; within the sandbox, loading the binary object into an executable memory region; performing a memory dump of the executable memory region; and analyzing the memory dump for malware characteristics.