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Network Traffic Intrusion Detection System Using Fuzzy Logic and Neural Network

Author

Listed:
  • Mrudul Dixit

    (Department of Electronics and Telecommunication, MKSSS's Cummins College of Engineering for Women, Pune, India)

  • Rajashwini Ukarande

    (Department of Electronics and Telecommunication, MKSSS's Cummins College of Engineering for Women, Pune, India)

Abstract

Intrusion Detection System (IDS) are actively used to identify any unusual activities in a network. To improve the effectiveness of IDS, security experts have embedded their extensive knowledge with the use of fuzzy logic, neuro-fuzzy, neural network and other such AI techniques. This article presents an intrusion detection system in network based on fuzzy logic and neural network. The proposed system is evaluated using the KDD Cup 99 dataset. The fuzzy system detects the intrusion behavior of the network using the defined set of rules. Whereas neural network trains the network based on the input and uses the trained system to predict the output. The evaluation depicts the effectiveness of the selected method in terms of selection of attributes which gives high True Positive Rate and True Negative Rate, with good precision in attack detection.

Suggested Citation

  • Mrudul Dixit & Rajashwini Ukarande, 2017. "Network Traffic Intrusion Detection System Using Fuzzy Logic and Neural Network," International Journal of Synthetic Emotions (IJSE), IGI Global, vol. 8(1), pages 1-17, January.
    • Handle: RePEc:igg:jse000:v:8:y:2017:i:1:p:1-17
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