Abstract:

Network assets are the sum of all digital assets such as equipment, information and applications owned by an organization in cyberspace that can be used by potential attackers. It is very important to identify network assets. In order to improve the efficiency and accuracy of network asset recognition, this paper designed a network asset recognition model based on graph neural network, which representd the asset response message in the form of a graph. The model could intuitively express the relationship between various elements in the text, and could use the connection relationship between nodes to retain the global graph information. The model consisted of three parts. Firstly, a heterogeneous graph containing three types of nodes and five types of edges was constructed based on the asset response message, then a two-level attention mechanism was introduced to train the two-layer convolutional neural network, and finally two types of loss functions were calculated and the final recognition results were obtained. Experiments using a sample set of 3000 network asset response messages achieves an identification accuracy of 92.38% after training, representing approximately 5% improvement over existing methods, which demonstrates the model’s effectiveness in asset recognition.

Key words: response message, heterogeneous graph, graph neural network, network asset identification