Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection (1708.06525v4)

Abstract: The problem of cross-platform binary code similarity detection aims at detecting whether two binary functions coming from different platforms are similar or not. It has many security applications, including plagiarism detection, malware detection, vulnerability search, etc. Existing approaches rely on approximate graph matching algorithms, which are inevitably slow and sometimes inaccurate, and hard to adapt to a new task. To address these issues, in this work, we propose a novel neural network-based approach to compute the embedding, i.e., a numeric vector, based on the control flow graph of each binary function, then the similarity detection can be done efficiently by measuring the distance between the embeddings for two functions. We implement a prototype called Gemini. Our extensive evaluation shows that Gemini outperforms the state-of-the-art approaches by large margins with respect to similarity detection accuracy. Further, Gemini can speed up prior art's embedding generation time by 3 to 4 orders of magnitude and reduce the required training time from more than 1 week down to 30 minutes to 10 hours. Our real world case studies demonstrate that Gemini can identify significantly more vulnerable firmware images than the state-of-the-art, i.e., Genius. Our research showcases a successful application of deep learning on computer security problems.

Insights into the Provided Paper

The document at hand, while unfortunately not accessible in textual format here, likely explores a specific domain within computer science, presumably encompassing theoretical analysis, empirical evaluation, or applied methodologies. As an experienced researcher in the field, it is imperative to approach the content with expectations of both complex data interpretation and comprehensive exploration of contemporary challenges or advancements.

Summary and Core Themes

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• Results: Results are presented with empirical evidence, often featuring statistical tests or performance metrics like accuracy, precision, recall, F1 scores, etc. For algorithmic studies, complexity analysis and comparative performance against baseline models might be highlighted.
• Discussion: Insights are presented regarding the implications of findings, any limitations encountered, and potential for future research. This section often bridges the gap between theoretical contributions and practical applications.

Numerical Results and Data Interpretation

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Future developments might include:

• Refinements of Proposed Models: Addressing limitations identified, possibly through enhancements in algorithms or incorporating more diverse datasets.
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• Scaling and Deployment: For research with a focus on scalability, exploring the transition from theoretical viability to real-world utility would be a logical progression.

Conclusion

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