Abstract: The problem of device configuration anomalies is becoming increasingly significant with the development of network communication equipment.Traditional detection tools usually only detect spelling,formatting and other issues,and cannot identify logic problems.Consequently,engineers’ experience plays a critical role in detecting such anomalies.To improve network service quality,reduce repetitive work,and address issues like slow detection speed,weak detection capabilities,and poor versatility of traditional tools,this paper draws on the design concept of abstract syntax trees and proposes an innovative unsupervised anomaly detection algorithm based on “configuration statement trees.” It can identify seven types of detectable anomalies and provides recommendations for anomaly localization and modification plans.The paper evaluates and compares the algorithm based on indicators such as detectable types,runtime,accuracy,and recall using configurations from the operator’s current network operation.The results demonstrate that the algorithm has good robustness and can effectively address network communication issues resulting from configuration anomalies in network communication equipment.

Key words: Anomaly detection, Cluster analysis, Automatic inspection of equipment, Abstract syntax tree, Co-occurrence corpus analysis, Unsupervised learning, Association analysis