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SAFE is a Self-Attentive neural network that takes as input a binary Function and creates an Embedding.
An embedding is vector of real numbers. The nice feature of SAFE embeddings is that two similar binary functions should generate two embeddings that are close in the metric space.
Good, read our paper on arXiv.
SAFE is available in our GitHub repository. Keep in mind that SAFE has been developed as a research project. We only provide a minimal working proof-of-concept, with the code and data to replicate our experiments. We are not responsible for any self-harm episode correlated with reading our (sometimes badly written) code.
If you are interested in this project write us an email.
SAFE has been designed and developed by:
* [Luca Massarelli](https://scholar.google.it/citations?user=mJ_QjZIAAAAJ&hl=it) (development and research) * [Giuseppe Antonio Di Luna](https://scholar.google.it/citations?hl=it&user=RgAfuVgAAAAJ&view_op=list_works&sortby=pubdate) (development and research) * [Fabio Petroni](https://scholar.google.it/citations?user=vxQc2L4AAAAJ&hl=it) (development and research) * [Leonardo Querzoni](https://scholar.google.it/citations?user=-_WFIJIAAAAJ&hl=it) (research) * [Roberto Baldoni](https://scholar.google.it/citations?user=82tR6VoAAAAJ&hl=it) (research)We are in debt with Google for providing free access to its cloud computing platform through the Education Program. Moreover, the authors would like to thank NVIDIA Corporation for partially supporting this work through the donation of a GPGPU card used during prototype development. This work is supported by a grant of the Italian Presidency of the Council of Ministers and by the CINI (Consorzio Interuniversitario Nazionale Informatica) National Laboratory of Cyber Security. Finally, we thank Davide Italiano for the insightful discussions.