Binary Code Embeddings for Natural-Language Security Analysis
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Solution Overview
Problem
Binary software code is not easily readable or analyzable by humans, making it challenging to determine security risks such as malware presence efficiently.
Innovation Solution
A system utilizing a file encoder model to generate a file embedding vector from binary code, combined with a natural language model to select relevant text descriptions from a data lake, enabling automated analysis of security risks through machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If binary software code is analyzed directly for security risks, then malware detection can be performed, but the analysis speed and accuracy are reduced due to the unreadable nature of binary code
Solution Approach 1:
The patent introduces an intermediary natural language description as a mediator between the binary software code and the security analysis process. The system generates natural language descriptions that explain the functionality and behavior of binary code, making it easier for security analysts to understand and analyze potential malware. This intermediary representation bridges the gap between machine-executable binary code and human-readable analysis, improving both accuracy and efficiency of security risk assessment
Solution Approach 2:
The patent replaces the traditional mechanical approach of directly examining binary code with an automated system that generates natural language descriptions using machine learning models. Instead of manually analyzing binary code or using simple pattern matching, the system substitutes a sophisticated automated process that converts binary code into comprehensible text descriptions, thereby improving analysis speed and accuracy simultaneously
2Productivity
If binary code is converted to natural language descriptions using machine learning, then readability and analysis efficiency improve, but system complexity increases
Solution Approach 1:
The patent segments the complex task of binary code analysis into multiple specialized components: a file encoder model that processes binary code, a natural language generation model that creates descriptions, and a security analysis component that evaluates risks. Each segment handles a specific aspect of the analysis, making the overall system more manageable and maintainable while improving productivity through specialized processing for each stage
3Productivity
If automated tools are used to retrieve and analyze code descriptions from data lakes, then analysis speed improves, but the complexity of data processing increases
Solution Approach 1:
The patent applies preliminary action by pre-processing binary code into natural language descriptions and storing them in a data lake before actual security analysis is needed. This advance preparation allows the system to quickly retrieve and analyze pre-generated descriptions during security assessments, improving analysis speed while the complexity is managed through automated preprocessing pipelines that run in advance
Data Source
AI summary
Systems, methods, and software can be used to determine 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 selecting one or more natural language description samples based on the file embedding vector and a distance function.