Behavior Computation for Binary Analysis
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Solution Overview
Problem
Current methods for analyzing software are inadequate in understanding the behavior of complex systems due to their complexity, leading to costly and reactive security measures, as they struggle to identify exploitable errors or malicious code without executing potentially harmful software.
Innovation Solution
The technology computes the behavior of software at machine speeds by transforming instructions into functional semantics, allowing for the analysis of binary executable files without source code execution, using Structure and Correctness Theorems to represent control structures and compute behavior signatures, enabling accurate and comprehensive analysis of both pre- and post-deployment software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional security analysis methods are used, then security measures can be implemented, but the analysis is reactive and costly due to inability to understand complex software behavior
Solution Approach 1:
The patent replaces traditional manual security analysis methods with an automated computational system that uses machine learning models to analyze software behavior. The system substitutes human experts with automated algorithms that can process software at machine speeds, transforming the mechanical process of security analysis from manual inspection to automated computational behavior computation.
Solution Approach 2:
The patent enables security analysis to be performed in advance before software deployment or execution. By computing software behavior statically without executing the code, the system provides preliminary security assessment that identifies potential threats before they can cause harm, eliminating the reactive nature of conventional security measures.
2Adaptability or versatility
If software complexity increases, then functionality improves, but understanding all execution paths becomes impossible with current methods
Solution Approach 1:
The patent introduces an intermediary computational layer that bridges the gap between complex software code and human understanding. The system uses behavior computation models as intermediaries to translate intricate execution paths into comprehensible behavior signatures, making complex software behavior analyzable without requiring experts to manually trace every execution path.
Solution Approach 2:
The patent transforms the analysis approach by changing parameters from examining individual execution paths to computing overall behavior signatures. Instead of tracking each possible path through complex code, the system computes aggregate behavior metrics that capture essential software functionality while remaining analyzable, effectively changing the dimensional parameters of analysis.
3Measurement precision
If manual security analysis is performed, then detailed understanding can be achieved, but costs and time increase significantly
Solution Approach 1:
The patent enables the security analysis system to perform its own analysis automatically without requiring continuous human intervention. The computational behavior model serves itself by automatically computing software behavior and generating security assessments, eliminating the need for manual analysis while maintaining high accuracy through algorithmic precision.
Solution Approach 2:
The patent introduces dynamic scaling to the analysis system, allowing it to adjust its computational depth and precision based on the specific software being analyzed. The system can dynamically allocate resources to achieve appropriate levels of analysis accuracy for different software complexities, maintaining precision while optimizing throughput through adaptive processing.
Data Source
AI summary
A method and system to detect behaviors of operational computer code. The method begins by tracking a synthetic call trace state variable when extracting the computed behavior of the program. The method continues by extending instruction semantics of call instructions with additional semantics by adding a current function call, either local or external API, to an existing call trace represented by the synthetic call trace state variable. A method finishes with extracting the computed behavior of a program.