Construction of network security vulnerability static detection model based on graph convolutional neural network

Yile Wang; Fang Xue

doi:10.1117/12.3026943

8 April 2024 Construction of network security vulnerability static detection model based on graph convolutional neural network

Yile Wang, Fang Xue

Author Affiliations +

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130901Y (2024) https://doi.org/10.1117/12.3026943
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

The current network vulnerability detection model adopts the scanning mode, ignoring the semantic relationship between network nodes, resulting in serious problems of missed and false detection, and low detection accuracy of the model. Model for optimizing the above defects, the Figure convolution neural network based network security holes static detection model. Abstract syntax tree tool is used to build the code attribute graph of network security vulnerability, and the vulnerability is statically analyzed according to the code attribute graph. The vulnerability detection model is constructed by taking the matrix composed of eigenvectors of neighboring basic block and the adjacency matrix of control flow graph as the input of graph convolution network model. In the detection model experiment, the average effective detection rate of the constructed model for vulnerabilities is 96.26%, and the missed detection rate and false detection rate are significantly lower than other methods, and the detection accuracy of the model is better.

Citation Download Citation

Yile Wang and Fang Xue "Construction of network security vulnerability static detection model based on graph convolutional neural network", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130901Y (8 April 2024); https://doi.org/10.1117/12.3026943

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