Flow control oriented forwarding and caching in cache-enabled networks

Wei, Bingjie; Wang, Lin; Zhu, Junlong; Zhang, Mingchuan; Xing, Ling; Wu, Qingtao

doi:10.1016/j.jnca.2021.103248

Cited by 1 publication

(1 citation statement)

References 37 publications

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“…The existing machine learning methods for traffic forwarding policy making are mainly divided into unsupervised learning methods and supervised learning methods [21,22]. Although unsupervised learning does not need to label abnormal sample data, it requires a large number of samples for training, and the model effect is not as good as supervised learning methods [21,23]. The supervised learning methods produce models with good adaptability by clustering abnormal sample data, but the common supervised learning methods also have the disadvantage of insufficient interpretability [21].…”

Section: Introductionmentioning

confidence: 99%

Random Fourier Approximation of the Kernel Function in Programmable Networks

Guo

Chen

et al. 2022

Applied Mathematics and Nonlinear Sciences

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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.

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