SENSE: An unsupervised semantic learning model for cross-platform vulnerability search

Li, Munan; Liu, Hongbo; Jiang, Xiangdong; Zhao, Zheng; Zhang, Tianhao

doi:10.1016/j.cose.2023.103500

Computers & Security

2023

DOI: 10.1016/j.cose.2023.103500

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SENSE: An unsupervised semantic learning model for cross-platform vulnerability search

Munan Li,

Hongbo Liu,

Xiangdong Jiang

et al.

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2024

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Cited by 2 publications

References 32 publications

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HAformer: Semantic fusion of hex machine code and assembly code for cross-architecture binary vulnerability detection

Jiang,

Wang,

Gong

et al. 2024

Computers & Security

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HAformer: Semantic fusion of hex machine code and assembly code for cross-architecture binary vulnerability detection

Jiang,

Wang,

Gong

et al. 2024

Computers & Security

View full text Add to dashboard Cite

GMN+: A Binary Homologous Vulnerability Detection Method Based on Graph Matching Neural Network with Enhanced Attention

Zhao,

Zhang,

Fan

et al. 2024

Applied Sciences

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The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the same weaknesses across multiple platforms. Deep learning has emerged as a promising approach for detecting homologous vulnerabilities in binary code due to their automated feature extraction and high efficiency. However, existing deep learning methods often struggle to capture deep semantic features in binary code, limiting their effectiveness. To address this limitation, this paper presents GMN+, which is a novel graph matching neural network with enhanced attention for detecting homologous vulnerabilities. This method comprehensively considers the information contained in instructions and incorporates types of input instruction. Masked Language Modeling and Instruction Type Prediction are developed as pre-training tasks to enhance the ability of GMN+ in extracting semantic information from basic blocks. GMN+ utilizes an attention mechanism to focus concurrently on the critical semantic information within functions and differences between them, generating robust function embeddings. Experimental results indicate that GMN+ outperforms state-of-the-art methods in various tasks and achieves notable performance in real-world vulnerability detection scenarios.

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SENSE: An unsupervised semantic learning model for cross-platform vulnerability search

Cited by 2 publications

References 32 publications

HAformer: Semantic fusion of hex machine code and assembly code for cross-architecture binary vulnerability detection

HAformer: Semantic fusion of hex machine code and assembly code for cross-architecture binary vulnerability detection

GMN+: A Binary Homologous Vulnerability Detection Method Based on Graph Matching Neural Network with Enhanced Attention

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