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VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity by VenkataKeerthy, S.; Banerjee, Soumya; Dey, Sayan; Andaluri, Yashas; PS, Raghul; Kalyanasundaram, Subrahmanyam; Pereira, Fernando Magno Quintão; Upadrasta, Ramakrishna is a scholarly article available to read on EtoBox.

What is VEXIR2Vec: An Architecture-Neutral Embedding Framework for Binary Similarity about?

Binary similarity involves determining whether two binary programs exhibit similar functionality, often originating from the same source code. In this work, we propose VexIR2Vec, an approach for binary similarity using VEX-IR, an architecture-neutral Intermediate Representation (IR). We extract the embeddings from sequences of basic blocks, termed peepholes, derived by random walks on the control-flow graph. The peepholes are normalized using transformations inspired by compiler optimizations. The VEX-IR Normalization Engine mitigates, with these transformations, the architectural and compiler-induced variations in binaries while exposing semantic similarities. We then learn the vocabulary of representations at the entity level of the IR using the knowledge graph embedding techniques in an unsupervised manner. This vocabulary is used to derive function embeddings for similarity assessment using VexNet, a feed-forward Siamese network designed to position similar functions closely and separate dissimilar ones in an n-dimensional space. This approach is amenable for both diffing and searching tasks, ensuring robustness against Out-Of-Vocabulary (OOV) issues. We evaluate VexIR2Vec on a

Author

VenkataKeerthy, S.; Banerjee, Soumya; Dey, Sayan; Andaluri, Yashas; PS, Raghul; Kalyanasundaram, Subrahmanyam; Pereira, Fernando Magno Quintão; Upadrasta, Ramakrishna

Published

2023

Language

EN