  In the field of cybersecurity, the ability to compute similarity scores at
the function level is import. Considering that a single binary file may contain
an extensive amount of functions, an effective learning framework must exhibit
both high accuracy and efficiency when handling substantial volumes of data.
Nonetheless, conventional methods encounter several limitations. Firstly,
accurately annotating different pairs of functions with appropriate labels
poses a significant challenge, thereby making it difficult to employ supervised
learning methods without risk of overtraining on erroneous labels. Secondly,
while SOTA models often rely on pre-trained encoders or fine-grained graph
comparison techniques, these approaches suffer from drawbacks related to time
and memory consumption. Thirdly, the momentum update algorithm utilized in
graph-based contrastive learning models can result in information leakage.
Surprisingly, none of the existing articles address this issue. This research
focuses on addressing the challenges associated with large-scale BCSD. To
overcome the aforementioned problems, we propose GraphMoco: a graph momentum
contrast model that leverages multimodal structural information for efficient
binary function representation learning on a large scale. Our approach employs
a CNN-based model and departs from the usage of memory-intensive pre-trained
models. We adopt an unsupervised learning strategy that effectively use the
intrinsic structural information present in the binary code. Our approach
eliminates the need for manual labeling of similar or dissimilar
information.Importantly, GraphMoco demonstrates exceptional performance in
terms of both efficiency and accuracy when operating on extensive datasets. Our
experimental results indicate that our method surpasses the current SOTA
approaches in terms of accuracy.
