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Research on Abnormal Data Analysis and Data Processing Method Based on Network Traffic

We studied an anomaly detection method based on a network traffic model starting with the intrusion detection of abnormal network traffic. The impact of data preprocessing on the detection model was analyzed by discussing the processing of original data. An anomaly detection method on the machine learning algorithm is proposed, which combines information gain and principal component analysis for the high-dimensional problem of data features. The experimental simulation data shows that the feature dimensionality reduction method improves the accuracy of the intrusion detection model compared with the traditional single feature dimensionality reduction method.