Summary of Is Function Similarity Over-engineered? Building a Benchmark, by Rebecca Saul et al.
Is Function Similarity Over-Engineered? Building a Benchmark
by Rebecca Saul, Chang Liu, Noah Fleischmann, Richard Zak, Kristopher Micinski, Edward Raff, James Holt
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to binary function similarity detection, a crucial task in security applications such as reverse engineering, malware analysis, and vulnerability detection. The authors identify discrepancies between current research and real-world needs, including data duplication and inaccurate labeling. To address this, they develop REFuSE-Bench, a high-quality benchmark consisting of datasets and tests that reflect real-world use cases. They evaluate machine learning models on Windows data, demonstrating that a simple baseline approach, focusing only on raw bytes, achieves state-of-the-art performance in multiple settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making it easier to analyze computer code. It’s an important task because hackers often try to hide their secrets by disguising malicious code as normal code. Right now, researchers are using complex tools and methods to compare pieces of code, but these approaches can be slow and unreliable. The authors of this paper think there must be a better way, so they created a new benchmark that tests different approaches to comparing code. They found that a simple approach, looking only at the raw bytes of the code, works surprisingly well and is faster than the complex methods used before. |
Keywords
* Artificial intelligence * Machine learning
