DETECTING DOS ATTACKS IN THE INTERNET OF THINGS (IOT) USING INTELLIGENT NEURAL NETWORK

Authors

  • Farzane Mohamady Master of Software engineering, Department of Computer, Islamic Azad University Varamin Branch, Varamin, Iran
  • Pouya Asadi Ph.D. of Software engineering, Department of Computer, Islamic Azad University Varamin Branch, Varamin, Iran
  • Akbar Morshed Aski Master of Software engineering, Department of Computer, Islamic Azad University Varamin Branch, Varamin, Iran

Keywords:

intrusion detection, cyberattacks, neural networks, data mining

Abstract

ICT is increasingly used in the last decade. One of the greatest challenges in this area is system and information security. This security is threatened by cyberattacks on networks. New techniques have been devised for the field by expanding research into network security. Among the most popular techniques, various data mining algorithms can be noted that are used either alone or in combination for intrusion detection. In this study, neural network technique was used for intrusion detection. The results of simulation of this technique on KDD dataset and MATLAB software indicated high precision of 98%. Comparison of this technique with other techniques such as CNN and BiLSTM suggested that these techniques had a less precision than the proposed one. In addition, the technique had a low error rate.

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Additional Files

Published

15-09-2019

How to Cite

Farzane Mohamady, Pouya Asadi, & Akbar Morshed Aski. (2019). DETECTING DOS ATTACKS IN THE INTERNET OF THINGS (IOT) USING INTELLIGENT NEURAL NETWORK. International Education and Research Journal (IERJ), 5(9). Retrieved from http://ierj.in/journal/index.php/ierj/article/view/1886