Improving the classification effectiveness of intrusion detection by using improved conditional variational autoencoder and deep neural_network

HIGHLIGHTS

• who: Yanqing Yang et al. from the School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China have published the paper: Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural_Network, in the Journal: Sensors 2019, 19, 2528 of 29/01/2018
• what: The authors propose a novel intrusion detection model that combines an improved conditional variational AutoEncoder (ICVAE) with a deep neural network (DNN) namely ICVAE-DNN. The authors compare the reconstruction loss l ( x̂, ŷ) with the maximum loss maxL j of the corresponding class . . .
  

   

  

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