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Double-Layer Positional Encoding embedding Method for Cross-Platform Binary Function Similarity Detection

Chinese Journal of Electronics 2022年第4期31卷 604-611页

作者： JIANG Xunzhi WANG Shen YU Xiangzhan GONG Yuxin School of Cyberspace Science Harbin Institute of Technology

The similarity detection between two cross-platform binary functions has been applied in many fields, such as vulnerability detection, software copyright protection or malware classification. Current advanced methods for binary function similarity detection usually use semantic features, but have certain limitations. For example, practical applications may encounter instructions that have not been seen in training, which may easily cause the out of vocabulary(OOV) problem. In addition, the generalization of the extracted binary semantic features may be poor, resulting in a lower accuracy of the trained model in practical applications. To overcome these limitations, we propose a double-layer positional encoding based transformer model(DP-Transformer). The DPTransformer's encoder is used to extract the semantic features of the source instruction set architecture(ISA),which is called the source ISA encoder. Then, the source ISA encoder is fine-tuned by the triplet loss while the target ISA encoder is trained. This process is called DPMIRROR. When facing the same semantic basic block,the embedding vectors of the source and target ISA encoders are similar. Different from the traditional transformer which uses single-layer positional encoding, the double-layer positional encoding embedding can solve the OOV problem while ensuring the separation between instructions, so it is more suitable for the embedding of assembly instructions. Our comparative experiment results show that DP-MIRROR outperforms the state-of-the-art approach, MIRROR, by about 35% in terms of precision at 1.

关键词： natural language processing embedding vectors single-layer positional encoding trained model encoding DP-MIRROR current advanced methods software copyright protection double-layer positional encoding based transformer model vocabulary Data security security of data cross-platform binary function similarity detection OOV problem cross-platform binary functions source ISA encoder extracted binary semantic features Computer vision and image processing techniques copyright traditional transformer vocabulary problem DP-Transformer's encoder source instruction Natural language processing encoders invasive software double-layer positional encoding embedding method vulnerability detection semantic basic block target ISA encoder instruction sets

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