Core Concepts

This paper proposes a method to simplify Mixed Boolean-Arithmetic (MBA) expressions using term rewriting techniques with the E-Graph data structure, which can efficiently represent multiple expressions with the same semantics. The approach aims to address the challenges of existing MBA deobfuscation techniques in terms of performance and preserving semantics.

Abstract

The paper discusses the problem of MBA obfuscation, where programs are transformed into a more complex form using a mixture of Boolean and arithmetic operations to impede reverse engineering and analysis. Existing deobfuscation techniques, such as those based on SMT solvers, have limitations in handling complex MBA expressions.
The key points are:
MBA expressions can be classified into linear and polynomial forms, with the latter being more challenging to simplify.
The E-Graph data structure is introduced as a way to efficiently represent and manipulate multiple expressions with the same semantics during the term rewriting process.
The paper describes the implementation of an MBA expression simplifier using the Rust E-Graph library, including preprocessing steps and the application of basic rewriting rules.
Experimental results are presented, showing that the proposed E-Graph-based approach can simplify a large portion of the tested MBA expressions, with reasonable performance compared to other deobfuscation techniques.
The authors identify the need to further improve the simplification of polynomial MBA expressions and explore the integration of constant folding techniques to enhance the overall deobfuscation capabilities.

Stats

The paper presents the following key statistics:
Tigress dataset: 323 total expressions, 267 successfully simplified (82.66% success rate), 69% simplification ratio, 3.98s average time.
Qsynth Custom EA dataset: 501 total expressions, 493 successfully simplified (98.40% success rate), 65.67% simplification ratio, 72.79s average time.
MBA Solver (Linear) dataset: 1008 total expressions, 818 successfully simplified (81.15% success rate), 93.26% simplification ratio, 41.13s average time.
MBA Solver (Non-polynomial) dataset: 1003 total expressions, 949 successfully simplified (94.61% success rate), 93.26% simplification ratio, 239.04s average time.
MBA Solver (Polynomial) dataset: 1008 total expressions, 587 successfully simplified (58.23% success rate), 94.91% simplification ratio, 27.15s average time.

Quotes

None.

Key Insights Distilled From

by Seoksu Lee,H... at **arxiv.org** 04-09-2024

Deeper Inquiries

To enhance the success rates of handling more complex polynomial MBA expressions, the E-Graph-based approach can be extended in several ways:
Advanced Rule Set: Introduce more sophisticated rewriting rules specifically tailored for polynomial expressions. By incorporating rules that target the unique characteristics of polynomial MBA expressions, such as nested terms and complex coefficients, the simplification process can be more effective.
Machine Learning Integration: Utilize machine learning techniques to identify patterns in polynomial MBA expressions that can guide the simplification process. By training models on a diverse set of polynomial expressions and their simplified forms, the system can learn to recognize optimal simplification strategies for different types of polynomials.
Constant Folding Optimization: Implement constant folding techniques to reduce the number of constant values in polynomial expressions. By identifying and folding constants within the expressions, the complexity can be significantly reduced, leading to more successful simplifications.
Parallel Processing: Employ parallel processing capabilities to handle the computational load of simplifying complex polynomial expressions. By distributing the workload across multiple cores or machines, the system can expedite the simplification process and improve overall success rates.

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.

The simplified MBA expressions derived through E-Graph-based techniques have diverse applications beyond malware analysis:
Program Optimization: Simplified MBA expressions can be utilized in program optimization tasks to enhance code efficiency and performance. By replacing complex expressions with simpler equivalents, the optimized code can execute more efficiently and consume fewer computational resources.
Formal Verification: In the realm of formal verification, simplified MBA expressions can aid in verifying the correctness of software systems. By transforming intricate expressions into more understandable forms, formal verification tools can analyze the behavior of the software more effectively and ensure its compliance with specified requirements.
Compiler Design: Simplified MBA expressions can play a crucial role in compiler design by facilitating the translation of high-level code into machine-readable instructions. By simplifying complex expressions during the compilation process, compilers can generate optimized code that executes more efficiently on target architectures.
Algorithm Analysis: In algorithm analysis, simplified MBA expressions can help in understanding the computational complexity of algorithms. By reducing complex expressions to simpler forms, researchers can analyze the efficiency and performance characteristics of algorithms more effectively.

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