Optimization of network security protection situation based on data clustering

With the emerging advancement and the latest study of the data science and computer engineering applications, network security protection is becoming a hog research topic. For the target of improving the network security and satefy protection ability, it is essential and also urget to evaluate the network security situation accurately, to realize the early interception and prevention of network attack. In view of the problem that the traditional fuzzy feature detection method is not good for the recent applications, this paper studies the novel method of optimization of network security protection situation based on data clustering. The network security state distribution model in complex network environment is constructed, the data mining of network security information is carried out by large data mining method, the adaptive feature extraction of the designed network security estimation status is extracted by the novel intrusion recognition and detection methodology, the feature data sets and the processing units of network security situation is extracted, and the fuzzy-C mean data clustering method is then applied to well extract the comprehensive information. The intrusion feature information flow is classified, the network security situation prediction is carried out according to the attribute classification results, and the security situation evaluation and the accurate evaluation are realized. For the verifications of the designed model, we arranged the experiment under the different scenarios. The simulation reflect that the designed framework suits for the network security considered scenario, the accuracy and robustness are both alidated.