A Review of Feature Reduction in Intrusion Detection System Based on Artificial Immune System and Neural Network

  • Uma Vishwakarma RITS, Bhopal
  • Prof. Anurag Jain RITS, Bhopal
  • Prof. Akriti Jain
Keywords: intrusion detection, feature reduction, artificial immune system and neural network.

Abstract

Feature reduction plays an important role in intrusion detection system. The large amount of feature in network as well as host data effect the performance of intrusion detection method. Various authors are research proposed a method of intrusion detection based on machine learning approach and neural network approach, but all of these methods lacks in large number of feature attribute in intrusion data. In this paper we discuss its various method of feature reduction using artificial immune system and neural network. Artificial immune system is biological inspired system work as mathematical model for feature reduction process. The neural network well knows optimization technique in other field. In this paper we used neural network as feature reduction process. The feature reduction process reduces feature of intrusion data those are not involved in security threats and attacks such as TCP protocol, UDP protocol and ICMP message protocol. This reduces feature-set of intrusion improve the classification rate of intrusion detection and improve the speed performance of the intrusion detection system. The current research going on fixed and static number of feature reduction, we proposed an automatic and dynamic feature reduction technique using PCNN network.

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Author Biographies

Uma Vishwakarma, RITS, Bhopal
Department of computer Science & Engg
Prof. Anurag Jain, RITS, Bhopal
Department of computer Science & Engg
Published
2013-07-15
How to Cite
Vishwakarma, U., Jain, P. A., & Jain, P. A. (2013). A Review of Feature Reduction in Intrusion Detection System Based on Artificial Immune System and Neural Network. INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 9(3), 1127-1133. https://doi.org/10.24297/ijct.v9i3.3338
Section
Articles