Detection of Abnormal Weak Correlated Data in Network Communication Based on Feature Analysis

With the continuous development and expansion of electronic technology, network communication began to appear abnormal communication, and abnormal network communication generated by weak correlation data is difficult to be eliminated. In order to solve the above problems, this paper proposes a detection method of abnormal weak correlation data in network communication based on feature analysis. The proposed method updates the basic detection principle of the traditional method and adds the steps to set abnormal weak correlation data feature types by using association rule to get more difference features between normal and abnormal data. The method tests abnormal flow data by using Netflow system, unifies data format, and extracts abnormal weak correlation data feature in abnormal flow according to coarse grain size representation. The information entropy is used to define the standard information entropy of abnormal weak correlation data. The weak correlation data is detected in fractal dimension for different time periods, and anomaly detection results are obtained. Experimental results show that the proposed method can effectively improve the adaptive ability of network communication.