Network Intrusion Detection Method Based on Naive Bayes Algorithm

In order to improve the intrusion detection ability of multi-dimensional node combination mixed topology network, this paper proposes an intrusion detection method based on naive Bayes algorithm. Build a distributed structure model of intrusion data in the network, and conduct traffic statistics and feature analysis on the network through low-speed monitoring and combined frequency scanning, so as to extract abnormal traffic label features of data in the network. Then, according to the types of attacks, Detect the fuzzy clustering center of intrusion data. The fusion model of anomaly feature distribution of intrusion traffic sequence is established based on the clustering results. Based on this, detect the redundancy and correlation of intrusion information, then analyze the fuzzy weight analysis of intrusion traffic sequence, and complete adaptive learning. Finally, control the attack data, so as to achieve the extraction and detection of intrusion information features. The test results show that the intrusion data detection results obtained by this method have high accuracy, so it has good detection performance and strong anti-interference ability, which can be used to improve the network security and anti attack ability.