Design of a linear regression model-based Internet exit anomaly detection method

Abstract

Abstract Anomaly detection for Internet egress is to enhance the user experience of browsing the Internet. Firstly, the five functional modules of the system are described, and the pre-processing data module is used to extract the Internet topology data for Internet anomaly detection. The linear regression algorithm is also introduced in detail, including the definition of linear regression and its parameter estimation method and the optimization of linear regression parameters by variance and squared error. Finally, the performance evaluation of the anomaly detection system proposed in this paper is carried out to verify the system’s feasibility. From the performance evaluation, the detection rate of the system in this paper is 2.93 and 5.33 percentage points higher than that of SVM and SNN detection methods, respectively, and the false alarm rate is 2.85%. Regarding the impact of different packet lengths, the system in this paper is relatively stable when the packet length is 600, with an accuracy rate of 99.94% and a false alarm rate of only 1.93%. The above data show that the Internet egress anomaly detection system proposed in this paper can effectively detect the anomalies existing in the Internet egress and accurately grasp the data can timely deal with the abnormal nodes, thus improving the user browsing experience.