Shallow neural network with kernel approximation for prediction problems in highly demanding data networks

作者：

Highlights：

• Most prediction problems of data networks are non-linear.

• Shallow neural networks are fast.

• Kernel approximation is a non-linear data transformation.

• Shallow neural networks with kernel approximation are non-linear and fast models.

• Proposed model is faster with prediction results comparable to deep models.

摘要

•Most prediction problems of data networks are non-linear.•Shallow neural networks are fast.•Kernel approximation is a non-linear data transformation.•Shallow neural networks with kernel approximation are non-linear and fast models.•Proposed model is faster with prediction results comparable to deep models.

论文关键词：Shallow neural network,Kernel approximation,Intrusion detection,Network traffic classification

论文评审过程：Received 29 October 2018, Revised 4 January 2019, Accepted 27 January 2019, Available online 28 January 2019, Version of Record 1 February 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.01.063