Performance Analysis of NSL_KDD Data Set Using Neural Networks with Logistic Sigmoid Activation Unit

  • Vignendra JannelaEmail author
  • Sireesha Rodda
  • Shyam Nandan Reddy Uppuluru
  • Sai Charan Koratala
  • G. V. S. S. S. Chandra Mouli
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 77)


Network intrusion detection system (NIDS) is a software tool that scans network traffic and performs security analysis on it. NIDS performs match operations upon passing traffic with a pre-established library of attacks in order to identify attacks or abnormal behavior. One of the standard data sets used widely for network intrusion systems is the NSL_KDD data set. The current paper aims to analyze the NSL_KDD data set using artificial neural network with sigmoid activation unit in order to perform a metric analysis study that is aimed at discovering the best fitting parameter values for optimal performance of the given data. Evaluation measures such as accuracy, F-measure, detection rate, and false alarm rate will be used to evaluate the efficiency of the developed model.


Network intrusion detection system NSL_KDD Neural networks Logistic sigmoid activation unit 



This work is supported by the Science and Engineering Research Board (SERB), Ministry of Science & Technology, Govt. of India under Grant No. SB/FTP/ETA-0180/2014.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Vignendra Jannela
    • 1
    Email author
  • Sireesha Rodda
    • 1
  • Shyam Nandan Reddy Uppuluru
    • 1
  • Sai Charan Koratala
    • 1
  • G. V. S. S. S. Chandra Mouli
    • 1
  1. 1.Department of Computer Science and Engineering, GITAM Institute of TechnologyGITAM UniversityVisakhapatnamIndia

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