GPCNKB: An Attack Prediction and Reasoning Model Based on an Improved Graph Convolutional Network

With the advancement of artificial intelligence technology, network attack scenarios are becoming increasingly intricate. On the one hand, the amount of attack knowledge is increasing; on the other hand, the potential relationships between these pieces of knowledge are becoming more difficult to discover and identify. Existing methods struggle to characterize these complex attack scenarios effectively and accurately predicting attack patterns. To address these issues, this paper introduces a novel network attack prediction and reasoning method, GPCNKB, which adopts the design idea of“classification-first, reasoning-later”. First, knowledge graphs and embedding techniques are used to represent attack scenarios, then graph convolutional networks (GCNs) are applied to classify the scenarios, and finally, the knowledge graph embedding model is utilized to reason the attack knowledge within scenarios of the same category. This design reduces the scope of reasoning and enhances its accuracy, enabling more effective network attack predictions. Additionally, the method incorporates concepts from evolutionary computation to refine the GCN classification model. This refinement optimizes the training parameters of the graph convolution network and improve the universality of the model. Experimental results reveal that GPCNKB exhibits notable advantages in reasoning speed and effectively uncovers potential relationships among attack knowledge within the same attack category. This work provides a novel approach for reasoning and predicting complex multi-step network attacks.