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A Network Intrusion Detection System with Hybrid Dimensionality Reduction and Neural Network Based Classifier

  • V. JyothsnaEmail author
  • A. N. Sreedhar
  • D. Mukesh
  • A. Ragini
Conference paper
  • 35 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1077)

Abstract

In the recent years, there is a lot of developments in the technology where lots and lots of information are crawling in the network. As the technology is increasing the threats and cyber attacks are also gradually increasing while is leading to evolve new security mechanisms. There are many classical security models such as firewalls, encryption, and authentication schemes. But these techniques are not able to secure today’s computers and networks from attacks. One of the best solutions for today’s network attacks is Intrusion Detection System (IDS). IDS are used to monitor and analyze the behavior of the network traffic. In this work, a novel network Intrusion Detection System with Hybrid Dimensionality Reduction and Neural Network Based Classifier is proposed. In this, Information Gain (IG) and Principal Component Analysis (PCA) are used for dimensionality reduction and multilayer perception technique is used to classify the data. The performance of this proposed method is estimated on benchmark dataset of network Intrusion Detection System i.e., NSL-KDD. The experimental results exhibit that the model designed has provided an improvement in accuracy and also provides less computational time and minimal false alarm rate.

Keywords

Network IDS Information Gain (IG) Principal Component Analysis (PCA) Multilayer perception Neural networks False alarm Performance Accuracy 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • V. Jyothsna
    • 1
    Email author
  • A. N. Sreedhar
    • 2
  • D. Mukesh
    • 2
  • A. Ragini
    • 2
  1. 1.Sree Vidyanikethan Engineering CollegeTirupatiIndia
  2. 2.Chadalawada Ramanamma Engineering CollegeTirupatiIndia

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