Network Intrusion Detection Method Based on Stacked Denoising Sparse Autoencoder and Extreme Learning Machine
Guoling Zhang, Xiaodan Wang, Rui Li, Jie Lai, Qian Xiang, Jiaxing He · 2020
Aiming at the problem of low detection accuracy and high false positive rate caused by noise doping in network data, and at the same time improving detection speed, a network intrusion detection based on stacked denoising sparse autoencoder and extreme learning machine (sDSAE-ELM) is proposed. First, the stacked denoising sparse autoencoder is used to automatically extract the robustness characteristics of the network data, and then the extreme learning machine is used for classification. Experiments on the NSL-KDD dataset show that the network intrusion detection method based on sDSAE-ELM has strong noise robustness when processing high-dimensional noisy data, while shortening the training time. And high detection accuracy and low false positive rate have been achieved.