Abnormal traffic identification algorithm of large-scale network based on linear discriminant analysis

With the increasing emphasis on network security, monitoring and identifying abnormal network traffic has become a research hotspot in the field of network security. In order to efficiently identify abnormal network traffic, this study proposes the use of linear discriminant analysis to process the data features of network traffic. The processing method is to separate different categories of features through mapping. Then, the principal component analysis method is used for feature normalization and a new feature matrix with low fit is constructed. The features in this matrix not only have independence, but also retain the differential characteristics of the samples. Finally, by introducing support vector machines to classify the feature matrix, the anomaly recognition of the samples is completed. The experimental results show that the feature extraction algorithm proposed in this study can achieve a maximum feature separation of 95%, and not only can complete classification within 39 ms as quickly as possible, but also has an error rate of only 0.5%. The classifier can achieve a maximum recognition rate of 98.6% for different types of abnormal traffic, and the highest accuracy rate can reach 99.1%. In summary, the improved classifier proposed in the study has excellent performance and can be used in network traffic recognition applications.