Research on Network Intrusion Detection Based on Cluster Learning Algorithm

With the rapid development of the Internet, there are many different forms of network attacks, and how to accurately identify network intrusion attacks has become the core of cyberspace security. There are many noise and redundancy terms in real-time network data, and the original intrusion detection technology has low accuracy and poor feature extraction ability. Therefore, this paper studies the network intrusion detection problem based on the cluster learning algorithm, establishes a network intrusion detection system based on the cluster learning algorithm, and uses the relevant feature selection to select the corresponding subset evaluation method to complete the feature selection, which can reduce the feature dimension, reduce data redundancy, significantly improve resource utilization, and reduce time complexity. After applying this algorithm, it can effectively improve the detection accuracy and detection rate of the system, which is an important way for network security detection.