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

<p>Random Fourier features represent one of the most influential and wide-spread techniques in machine learning to scale up kernel algorithms. As the methods based on random Fourier approximation of the kernel function can overcome the shortcomings of machine learning methods that require a large number of labeled sample, it is effective to be applied to the practical areas where samples are difficult to obtain. Network traffic forwarding policy making is one such practical application, and it is widely concerned in the programmable networks. With the advantages of kernel techniques and random Fourier features, this paper proposes an application of network traffic forwarding policy making method based on random Fourier approximation of kernel function in programmable networks to realize traffic forwarding policy making to improve the security of networks. The core of the method is to map traffic forwarding features to Hilbert high-dimensional space through random Fourier transform, and then uses the principle of maximum interval to detect adversarial samples. Compared with the traditional kernel function method, it improves the algorithm efficiency from square efficiency to linear efficiency. The AUC on the data set from real-world network reached 0.9984, showing that the method proposed can realize traffic forwarding policy making effectively to improve the security of programmable networks.</p>
</abstract>

Random Fourier features represent one of the most influential and wide-spread techniques in machine learning to scale up kernel algorithms. As the methods based on random Fourier approximation of the kernel function can overcome the shortcomings of machine learning methods that require a large number of labeled sample, it is effective to be applied to the practical areas where samples are difficult to obtain. Network traffic forwarding policy making is one such practical application, and it is widely concerned in the programmable networks. With the advantages of kernel techniques and random Fourier features, this paper proposes an application of network traffic forwarding policy making method based on random Fourier approximation of kernel function in programmable networks to realize traffic forwarding policy making to improve the security of networks. The core of the method is to map traffic forwarding features to Hilbert high-dimensional space through random Fourier transform, and then uses the principle of maximum interval to detect adversarial samples. Compared with the traditional kernel function method, it improves the algorithm efficiency from square efficiency to linear efficiency. The AUC on the data set from real-world network reached 0.9984, showing that the method proposed can realize traffic forwarding policy making effectively to improve the security of programmable networks.

Random Fourier Approximation of the Kernel Function in Programmable Networks

College of Arts & Science, Applied Science University

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Random Fourier Approximation of the Kernel Function in Programmable Networks"}

Random Fourier features represent one of the most influential and wide-spread techniques in machine learning to scale up kernel algorithms. As the methods...

Random Fourier Approximation of the Kernel Function in Programmable Networks

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 1831 - 1840

Eingereicht: 26. Apr. 2022

Akzeptiert: 21. Juni 2022

DOI: https://doi.org/10.2478/amns.2022.2.0172

Schlüsselwörter
random Fourier features, kernel function, programmable networks, SDN, P4, traffic forwarding policy making

© 2023 Wei Guo et al., published by Sciendo

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

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Random Fourier Approximation of the Kernel Function in Programmable Networks

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 1831 - 1840

Eingereicht: 26. Apr. 2022

Akzeptiert: 21. Juni 2022

DOI: https://doi.org/10.2478/amns.2022.2.0172

Schlüsselwörterrandom Fourier features, kernel function, programmable networks, SDN, P4, traffic forwarding policy making

© 2023 Wei Guo et al., published by Sciendo

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

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Schlüsselwörter
random Fourier features, kernel function, programmable networks, SDN, P4, traffic forwarding policy making