Binary Code Similarity Detection (BCSD) is essential in various binary code security applications, enabling tasks such as vulnerability identification, malware analysis, and detection of code plagiarism. With the growing adoption of deep neural networks (DNNs) in BCSD, there has been significant progress in the identification and classification of similar code segments. However, DNN-based BCSD approaches often suffer from high false positive rates, because DNNs inevitably map different binary functions with complex structures and semantics to similar low-dimensional embeddings.

To alleviate this issue, this paper introduces BinEGA, a novel graph alignment-based approach to enhance the accuracy of DNN-based BCSD approaches. The main idea of BinEGA is to employ a general and low-cost equivalence check through lightweight graph alignment, allowing for the identification and elimination of semantically deviating functions among the top-k candidates retrieved by DNN-based BCSD approaches. During the graph alignment process, we first obtain the node embeddings according to structure and attribute feature. Then we employs pairwise comparison of these node embeddings to filter the false positives because binary code compiled from the same source code always shares similar basic blocks. Our experimental results demonstrate that BinEGA effectively enhances the performance of various edge-cutting DNN-based BCSD approaches across diverse scenarios. For instance, BinEGA significantly enhances RECALL@10 in the cross-optimization scenario for state-of-the-art (SOTA) approaches, with an average improvement of 29.2% for BinaryAI and 33.5% for jTrans. Moreover, BinEGA achieves 88.9% reduction in execution time compared to other enhancement techniques. In summary, this work provides a robust, generalizable, and efficient solution to improve the reliability of BCSD tools in real-world applications.