Binary Code Description via File Embeddings for Security Analysis
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Solution Overview
Problem
Binary software code is not easily readable or analyzable by humans, making it difficult to assess security risks such as malware presence effectively.
Innovation Solution
A system utilizing a file encoder model to generate a file embedding vector from binary code, combined with a large language model (LLM) to produce a natural language description of the code, leveraging machine learning and natural language processing to enhance security analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If binary software code is analyzed directly, then security risk detection can be performed, but the code is not easily readable or analyzable by humans
Solution Approach 1:
The patent introduces an intermediary system consisting of a file encoder model and a large language model that translates binary code into natural language descriptions. The file encoder model processes the binary code to extract features, and the LLM generates human-readable descriptions of security risks, thereby bridging the gap between machine analysis and human understanding without compromising detection accuracy
Solution Approach 2:
The patent replaces the traditional mechanical approach of direct binary code analysis with an automated machine learning system. Instead of requiring manual analysis of binary code by security experts, the system uses trained models to automatically generate natural language descriptions, substituting human cognitive effort with automated intelligence while maintaining or improving detection reliability
2Reliability
If machine learning models are used to detect malware, then security risk detection accuracy is improved, but the analysis process becomes more complex
Solution Approach 1:
The patent divides the complex malware detection system into distinct modular components: a file encoder model for feature extraction, a large language model for description generation, and separate processing stages. This segmentation allows each component to be independently trained, optimized, and maintained, reducing overall system complexity while preserving detection accuracy
Solution Approach 2:
The patent employs a universal large language model that can handle multiple security analysis tasks through natural language generation. The same LLM architecture can describe different types of security risks, malware behaviors, and code characteristics, reducing the need for multiple specialized models and simplifying the overall system
3Ease of operation
If natural language descriptions are generated for binary code, then user-friendliness is improved, but the processing time may increase
Solution Approach 1:
The patent performs preliminary action by training the file encoder model and LLM in advance on large datasets of binary code and corresponding security descriptions. Once trained, the models can rapidly generate natural language descriptions during actual security analysis without requiring time-consuming manual interpretation, thus reducing processing time while maintaining user-friendliness
Solution Approach 2:
The patent creates a copy or representation of the binary code's security characteristics in natural language form through the LLM. Instead of requiring users to directly interpret complex binary data, the system generates a textual copy that preserves the essential security information in an easily consumable format, improving user-friendliness without significantly increasing processing time
Data Source
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AI summary
Systems, methods, and software can be used to generate natural language description of a software code. In some aspects, a method includes: processing a binary code by using a file encoder model to obtain a file embedding vector; and processing the file embedding vector to obtain a text description of the binary code by using a large language model (LLM).