Neural Code Obfuscation for Reverse-Engineering Resistance
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Solution Overview
Problem
Existing code obfuscation techniques, including neural networks, are vulnerable to reverse engineering due to their determinate nature and opacity, making them ineffective in whitebox security scenarios.
Innovation Solution
Utilizing a neural network to approximate an obfuscation function, creating an obfuscated code portion with additional outputs that do not correspond to the original function inputs, thereby increasing complexity and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If code obfuscation techniques are applied to protect intellectual property, then security against reverse engineering is improved, but code complexity and processing overhead increase
Solution Approach 1:
The patent replaces traditional mechanical/code-based obfuscation methods with a neural network system. The neural network automatically performs function identification, transformation code generation, and validation, substituting manual or rule-based obfuscation processes with an intelligent system that reduces complexity while maintaining security.
Solution Approach 2:
The obfuscation system performs self-validation through the neural network, which automatically verifies that transformed code maintains equivalent functionality. The system self-corrects and self-optimizes by learning from validation results, reducing the need for external verification and manual intervention.
2Reliability
If traditional obfuscation methods are used, then some level of protection is achieved, but they fail to prevent determined reverse engineers and add significant overhead
Solution Approach 1:
The patent replaces traditional obfuscation methods with a neural network-based system that learns optimal obfuscation strategies. This intelligent system processes code more efficiently than rule-based methods while providing adaptive protection that evolves against new reverse engineering techniques.
Solution Approach 2:
The neural network dynamically adjusts obfuscation parameters based on the specific code being protected and threat patterns. This allows the system to optimize the balance between protection strength and processing overhead, applying stronger obfuscation only where necessary rather than uniformly across all code.
3Reliability
If complex obfuscation transformations are applied, then security against analysis is improved, but the transformed code becomes harder to debug and maintain
Solution Approach 1:
The patent implements a feedback mechanism where the neural network validates transformed code against the original functionality. This feedback loop ensures that obfuscation transformations preserve semantic meaning and control flow, making the code more maintainable while still providing security against analysis.
Solution Approach 2:
The system adjusts obfuscation parameters to balance security and debuggability. By controlling the degree and type of transformation applied, the system maintains enough structure for debugging while applying sufficient obfuscation to protect against analysis.
Data Source
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AI summary
Systems, methods, and storage media for creating secured transformed code from input code, the input code having at least one code function that includes at least one function value are disclosed. Exemplary implementations may: receive input code; apply an obfuscation algorithm to at least a portion of a selected code function of the input code to thereby create an obfuscated code portion having at least one obfuscated value that is different from the at least one function value; and store the obfuscated code portion on non-transient computer media to create obfuscated code having substantially the same function as the input code.