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Intrusion Detection System Using Random Forest on the NSL-KDD Dataset

  • Prashil Negandhi
  • Yash TrivediEmail author
  • Ramchandra Mangrulkar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 906)

Abstract

In the modern world of interconnected systems, network security is gaining importance and attracting a lot of new research and study. Intrusion detection systems (IDSs) form an integral part of network security. To enhance the security of a network, machine learning algorithms can be applied to detect and prevent network attacks. Taking advantage of the robust NSL-KDD dataset, we have employed the supervised learning algorithm random forests to train a model to detect various networking attacks. To further increase the classification accuracy of our model, we have employed the use of famous data mining technique of feature selection. Smart feature selection using Gini importance has been employed to reduce the number of features. Experimental results have shown that our model not only runs faster but also performs with a higher accuracy.

Keywords

NSL-KDD Machine learning Random forest Classification Computer networks Cybersecurity 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Prashil Negandhi
    • 1
  • Yash Trivedi
    • 1
    Email author
  • Ramchandra Mangrulkar
    • 1
  1. 1.Department of Computer EngineeringDwarkadas J. Sanghvi College of EngineeringMumbaiIndia

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