<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Design of a linear regression model-based Internet exit anomaly detection method"}

Anomaly detection for Internet egress is to enhance the user experience of browsing the Internet. Firstly, the five functional modules of the system are...

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

Publié en ligne: 26 août 2023

Pages: -

Reçu: 08 sept. 2022

Accepté: 06 mars 2022

DOI: https://doi.org/10.2478/amns.2023.2.00248

Mots clés
Linear regression, Internet topology, Egress anomaly detection, Squared error, Performance evaluation

© 2023 Mi Yan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Publié en ligne: 26 août 2023

Pages: -

Reçu: 08 sept. 2022

Accepté: 06 mars 2022

DOI: https://doi.org/10.2478/amns.2023.2.00248

Mots clésLinear regression, Internet topology, Egress anomaly detection, Squared error, Performance evaluation

© 2023 Mi Yan, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Linear regression, Internet topology, Egress anomaly detection, Squared error, Performance evaluation