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arXiv · 1810.09683

Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks

Roberto Baldoni·Giuseppe Antonio Di Luna·Luca Massarelli·Fabio Petroni·Leonardo Querzoni
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Abstract

In this paper we consider the binary similarity problem that consists in determining if two binary functions are similar only considering their compiled form. This problem is know to be crucial in several application scenarios, such as copyright disputes, malware analysis, vulnerability detection, etc. The current state-of-the-art solutions in this field work by creating an embedding model that maps binary functions into vectors in $\mathbb{R}^{n}$. Such embedding model captures syntactic and semantic similarity between binaries, i.e., similar binary functions are mapped to points that are close in the vector space. This strategy has many advantages, one of them is the possibility to precompute embeddings of several binary functions, and then compare them with simple geometric operations (e.g., dot product). In [32] functions are first transformed in Annotated Control Flow Graphs (ACFGs) constituted by manually engineered features and then graphs are embedded into vectors using a deep neural network architecture. In this paper we propose and test several ways to compute annotated control flow graphs that use unsupervised approaches for feature learning, without incurring a human bias. Our methods are inspired after techniques used in the natural language processing community (e.g., we use word2vec to encode assembly instructions). We show that our approach is indeed successful, and it leads to better performance than previous state-of-the-art solutions. Furthermore, we report on a qualitative analysis of functions embeddings. We found interesting cases in which embeddings are clustered according to the semantic of the original binary function.

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Roberto Baldoni, Giuseppe Antonio Di Luna, Luca Massarelli, Fabio Petroni, Leonardo Querzoni. 2018-11-13. Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks. https://arxiv.org/abs/1810.09683

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