Construction of a computer network fault analysis and intrusion detection system based on K-means clustering algorithm

In order to improve the security of user access to computer networks, this paper is based on the K-means clustering algorithm, which first extracts the network text features through network data analysis and data pre-processing. Then the K-means clustering algorithm is used to cluster the network data and identify different data patterns. Then, feature analysis is performed on each data pattern to extract the features related to network failure and intrusion to build a failure analysis and intrusion detection system, which is combined with the gradient decomposition algorithm for optimization. The results of simulation experiments in the same environment show that the fault analysis accuracy of the built platform is up to 100% and the number of fault misses is controlled within 5. It is thus clear that the proposed system can effectively identify network faults and intrusions with high detection accuracy and robustness. Therefore, the system is of practical value in real applications.