Abstract: Aiming at the many causes and difficult features of internal network security threat nodes, an internal network security threat detection method based on XGBoost algorithm is proposed. Using the state differences between the internal network communities as an indicator, the edge weights of the nodes within different community types are calculated to find the nodes associated with the target values. Eigenvalues extracted through multiple assignments are taken as the initial input value XGBoost decision tree to construct the threat feature objective function, solve the corresponding Taylor coefficient of each node, and realize internal network security threat detection. The experimental data show that the proposed method has high feature extraction accuracy and can achieve accurate detection under various network attack conditions.

Key words: XGBoost algorithm, security threat detection, objective function, taylor coefficient; network community