CIDER Binary Analysis via Execution Trace Translation
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Solution Overview
Problem
Current methods for generating program descriptions from binary files are inefficient, relying on formal methods that require extensive computational resources and human analysis, and are not suitable for broadly applicable program understanding due to their reliance on irrelevant function analysis and fractal-like code structures.
Innovation Solution
The CIDER system employs a sequence-to-sequence transformer model to translate execution traces into human-language descriptions, allowing for the quick understanding of executable files by cyber analysts, reducing the need for manual analysis and leveraging machine learning to summarize function logic and data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If formal methods are used to discover primitive properties of binary code, then analysis precision is improved, but computing time increases significantly and resource consumption increases
Solution Approach 1:
The patent replaces formal verification methods with a machine learning-based approach. Instead of using computationally intensive formal methods to analyze binary code, the system trains a neural network model on execution traces and uses it to generate human language descriptions of code functionality. This substitution of mechanical/formal analysis with learned patterns dramatically reduces computing time while maintaining useful analysis precision.
Solution Approach 2:
The patent creates a virtual model of code execution by recording execution traces and using them to train a neural network. Rather than directly analyzing the binary code structure, the system copies execution behavior into trace data and learns from these copies, enabling faster analysis without sacrificing understanding of code properties.
2Loss of information
If formal methods are used to analyze binary code, then certain properties can be discovered, but the output requires human analysis to be understood and is not broadly applicable
Solution Approach 1:
The patent introduces an intermediary layer between binary code analysis and human understanding. Instead of directly outputting formal verification results that require expert interpretation, the system uses a neural network to translate code execution patterns into natural language descriptions. This intermediary translation layer makes the output accessible to non-experts while preserving the technical accuracy of the analysis.
Solution Approach 2:
The patent changes the output parameter from formal logic representations to human language text. By transforming the nature of the output from technical formalisms to natural language, the system makes program understanding accessible to a broader audience without losing the essential information about code functionality.
3Difficulty of detecting and measuring
If Ghidra is used for reverse engineering with multiple logic models, then analysis capability is improved, but extensive reverse engineering experience and machine code knowledge are required
Solution Approach 1:
The patent enables the system to perform the work that previously required expert human analysis. By training a neural network on execution traces, the system automatically generates functional descriptions of code without requiring users to have deep reverse engineering expertise. The system serves itself by learning patterns from data, eliminating the need for human experts to manually interpret multiple logic models.
Solution Approach 2:
The patent replaces the complex interaction between human experts and multiple logic models with an automated machine learning system. Instead of requiring users to navigate Ghidra's sophisticated but complex interface and interpret multiple representations, the system automatically processes execution traces and generates understandable descriptions, substituting expert human cognition with learned artificial intelligence.
4Measurement precision
If manual analysis of functions is performed to understand data types and logic, then accurate program understanding is achieved, but time consumption increases significantly
Solution Approach 1:
The patent performs preliminary analysis by recording execution traces before the actual analysis task. These traces capture the actual runtime behavior of the code, including data type usage and control flow patterns. By having this information prepared in advance, the neural network can quickly learn accurate program semantics without requiring time-consuming manual analysis during the actual reverse engineering task.
Solution Approach 2:
The patent substitutes manual human analysis with automated neural network processing. The system replaces the slow, iterative process of human analysts examining functions and inferring data types with rapid automated pattern recognition based on execution traces, dramatically increasing analysis speed while maintaining accuracy.
Data Source
AI summary
Devices, systems, and methods for executable binary file description are provided. A method can include generating or identifying an execution trace for the executable binary file. The method can include operating, based on the execution trace, a transformer model that generates a human language program description of the execution trace.