Combining E-Graph-based simplification with other techniques can significantly enhance deobfuscation performance:
Machine Learning: By integrating machine learning algorithms, the system can learn from a vast dataset of obfuscated code and their corresponding simplified forms. Machine learning models can assist in identifying complex patterns in the obfuscated code and suggest optimal simplification strategies based on learned patterns.
Program Synthesis: Program synthesis techniques can be used to automatically generate simplified code snippets that preserve the functionality of the original obfuscated code. By synthesizing simpler code representations from the obfuscated code, the deobfuscation process can be streamlined and made more efficient.
Constraint Solving: Incorporating constraint solving methods can help in identifying constraints within the obfuscated code and deriving simpler expressions that satisfy these constraints. By leveraging constraint solvers in conjunction with E-Graph-based simplification, the system can effectively navigate the deobfuscation process.
Deep Learning: Deep learning models, such as neural networks, can be employed to analyze the structural and semantic properties of obfuscated code. By training neural networks on a diverse set of obfuscated code samples, the system can learn to generate simplified representations that capture the essence of the original code while removing unnecessary complexity.