Resumen de Random Fourier Approximation of the Kernel Function in Programmable Networks
Wei Guo, Yue He, Hexiong Chen, Feilu Hang, Jun Zhang, Samer Shorman
Random Fourier features represent one of the most influential and wide-spread techniques in machine learning toscale up kernel algorithms. As the methods based on random Fourier approximation of the kernel function canovercome 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 forwardingpolicy making is one such practical application, and it is widely concerned in the programmable networks. Withthe advantages of kernel techniques and random Fourier features, this paper proposes an application of networktraffic forwarding policy making method based on random Fourier approximation of kernel function inprogrammable 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 randomFourier transform, and then uses the principle of maximum interval to detect adversarial samples. Comparedwith 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 proposedcan realize traffic forwarding policy making effectively to improve the security of programmable networks.
