Long Short-Term Memory-Based Recurrent Neural Network Approach for Intrusion Detection

  • Nishanth Rajkumar
  • Austen D’Souza
  • Sagaya Alex
  • G. Jaspher W. KathrineEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Intrusion detection is very essential in the field of information security. The cornerstone of an Intrusion Detection System (IDS) is to accurately identify different attacks in a network. In this paper, a deep learning system to detect intrusions is proposed. The existing recurrent neural network (RNN-IDS) based IDS is expanded to include Long Short term memory (LSTM) and the results are compared. The binary classification performance of the RNN-IDS is tested with various learning rates and using different number of hidden nodes. The results show that by integrating LSTM with RNN-IDS, the accuracy of intrusion prediction has improved against the benchmark dataset.


Intrusion detection Recurrent neural network Long short term memory Deep learning 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nishanth Rajkumar
    • 1
  • Austen D’Souza
    • 1
  • Sagaya Alex
    • 1
  • G. Jaspher W. Kathrine
    • 1
    Email author
  1. 1.Karunya Institute of Technology and SciencesCoimbatoreIndia

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