Iot Network Intrusion Alarming System Using Bat Algorithm & Neural Network.

The scope of IoT networks has expanded drastically in the modern digital environment. Connectivity along with use of the internet, particularly in the form of data collection and exchange, is a primary characteristic of devices in an IoT network. This paper has developed a (Bat IOT Network Intrusion...

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Publicado en:Journal of Namibian Studies Vol. 33; pp. 4845 - 4861
Autores principales: Chaudhary, Diwakar Kumar, Yadav, Pritaj, Jha, Kanchan
Formato: Artículo
Publicado: Society of Cultural Studies & Social Sciences 2023 Supplement
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Iot Network Intrusion Alarming System Using Bat Algorithm & Neural Network.
      aug:
        au:
          Chaudhary, Diwakar Kumar
          Yadav, Pritaj
          Jha, Kanchan
        affil:
          Computer Science and Engineering, Ravindranath Tagore University, Bhopal, India.
          School of Advance Computing, Sanjeev Agrawal Educational Global University, Bhopal, India.
      su:
        Electronic alarm systems
        Internet of things
        Algorithms
        Digital technology
        Bats
        Memetics
        Roosting
      sug:
        subj:
          Electronic alarm systems
          Internet of things
          Algorithms
          Digital technology
          Bats
          Memetics
          Roosting
      keyword:
        Deep Learning
        Feature Optimization
        Genetic Algorithm
        Intrusion Detection
        Soft Computing
      ab: The scope of IoT networks has expanded drastically in the modern digital environment. Connectivity along with use of the internet, particularly in the form of data collection and exchange, is a primary characteristic of devices in an IoT network. This paper has developed a (Bat IOT Network Intrusion Detection System (BINIDS) that alarms for the intrusion in the IOT network. Paper has work on the input dataset for improving the learning of neural network. Dataset optimization was done by BAT algorithm. Input features were cluster into two category selected and rejected. All selected features were processed to train the neural network. Experiment was done on real IOT dataset under different testing size. Result shows that proposed model has improved the work performance.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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