Summary of Blens: Contrastive Captioning Of Binary Functions Using Ensemble Embedding, by Tristan Benoit et al.
BLens: Contrastive Captioning of Binary Functions using Ensemble Embedding
by Tristan Benoit, Yunru Wang, Moritz Dannehl, Johannes Kinder
First submitted to arxiv on: 12 Sep 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 This paper presents a novel approach to predicting function names in stripped binaries using advances in automated image captioning. The proposed model, BLens, combines multiple binary function embeddings into a new ensemble representation and aligns it with the name representation latent space via contrastive learning. A transformer architecture is used to generate function names. The experiments demonstrate that BLens significantly outperforms the state of the art in various settings, including per-binary, cross-project, and experimental settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to predict function names in computer code using ideas from image captioning. The model, called BLens, takes different parts of the code and matches them with words that describe what those parts do. It uses special algorithms to make this connection and then generates the actual function names. This approach is better than current methods at naming functions, especially when it’s working on new projects. |
Keywords
» Artificial intelligence » Image captioning » Latent space » Transformer
