A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network

HIGHLIGHTS

• who: Yakub Kayode Saheed from the University of Calabria, Italy have published the research work: A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the internet of things network, in the Journal: (JOURNAL)
• what: The authors provide an anomaly-based IDS methodology for IoT networks in this paper. The authors propose a novel hybrid Autoencoder and Modified Particle Swarm Optimization (HAEMPSO) for feature selection and deep neural_network (DNN) for classification. By weighing three specified constants, the study proposes a comprehensive system approach for optimizing the search process in SSO . . .
  

   

  

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