Potential relation mining in internet of things threat intelligence knowledge graph
Knowledge graph plays a crucial role in the sharing and utilization of Internet of Things Threat Intelligence(ITI).Graph Neural Network(GNN)can be applied to tasks of knowledge representation in ITI Knowledge Graph(ITIKG),thereby mining potential relations in ITIKG.However,most existing GNNs fail to consider the influence of node types on node representation capability and employ random sampling strategies for node sampling during node information aggregation,leading to an inability to distinguish neighbors at different distances and a lack of consideration for correlations among or importance of nodes.To address these issues,firstly ITIKG was constructed on the basis of various data sources.Subsequently,a deterministic sampling method was designed to sample the neighbors of root node based on node importance,and consider the distance between neighbors and root node,as well as the centrality measurement of neighbors in the graph,namely Katz centrality and betweenness centrality.Finally,embedding and aggregation methods of node,node modality,and node type were devised.On this basis,a Deterministic Multimodal Heterogeneous Graph Neural Network(DM-HGNN)model was proposed.Experimental results on link prediction in the constructed ITIKG demonstrate that the performance of DM-HGNN model is better than that of knowledge representation models such as metapath2vec,Multi-modal Knowledge Graph Representation Learning(MMKRL),and Complex Graph Convolutional Network(ComplexGCN).Compared to the suboptimal model MMKRL,DM-HGNN model exhibits an improvement of 6.8%in Area Under the Curve(AUC)and 7.1%in F1-score,indicating the effectiveness and advancement of DM-HGNN model in link prediction tasks.
Internet of Things(IoT)securitythreat intelligenceknowledge graphGraph Neural Network(GNN)knowledge representationlink prediction
万方数据
维普
