Depth Detection Method for Information Security Vulnerabilities in Heterogeneous Networks Based on Graph Neural Network

FEI Shengxiang; CHEN Zilong; WANG Chong; WANG Rui; LIU Xinpeng

doi:10.13512/j.hndz.2024.03.20

Abstract：Influenced by the diversity and interoperability of connected devices,the relationship between hetero-geneous network nodes is complex.Therefore,it usually leads to the lack of effective capture of the relationship be-tween nodes when detecting security vulnerabilities,resulting in poor detection accuracy.To solve this problem,this paper proposed a depth detection method for information security vulnerabilities in heterogeneous networks based on a graph neural network.Heterogeneous network entities were regarded as graph nodes,and the relation-ship between different entities was regarded as edges.The heterogeneous network was transformed into a graph repre-sentation,and the node and edge information were extracted by adjacency matrix and weight matrix,respectively.GraphSAGE network model in the field of graph neural network was used to deal with the nodes and edges in the het-erogeneous network,and an attention mechanism was introduced to learn the feature representation of nodes.The feature vectors of nodes and edges in the heterogeneous network were used as data inputs,and a classifier was con-structed by using the random forest algorithm and trained to make it determine whether there are security vulnerabili-ties based on the attribute information of edges and nodes.Finally,the input samples were classified by voting meth-od.In the experiment,the detection accuracy of the proposed method was tested.The final test results show that when the proposed method is used to detect security vulnerabilities in the heterogeneous network,the matching de-gree of vulnerability risk levels is high,and the detection accuracy is ideal.