To explore the efficiency of Cluster-GCN on Cybersecurity

The detection and protection of Cyberattacks are always challenging due to the complexed and varied Cyberattack schemes. Research on knowledge graphs is becoming increasingly mature in many fields. To construct a cybersecurity knowledge base, researchers have combined the concept of the knowledge graph with cybersecurity. In this paper, we apply cluster-based graph convolutional network (Cluster-GCN) to explore the graph cluster structure, which emphasizes the harmful vulnerability and exposure for a system that providing a prevention of potential malicious attack. In order to explore the efficiency of Cluster-GCN on vulnerabilities detection, we also compare it with convolutional neural network and traditional graph convolutional network.