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The Journal of Supercomputing

, Volume 74, Issue 11, pp 5797–5812 | Cite as

An efficient cascaded method for network intrusion detection based on extreme learning machines

  • Yuanlong Yu
  • Zhifan Ye
  • Xianghan ZhengEmail author
  • Chunming Rong
Article
  • 371 Downloads

Abstract

Machine learning techniques are widely used for network intrusion detection (NID). However, it has to face the unbalance of training samples between classes as it is hard to collect samples of some intrusion classes. This would produce false positives for these intrusion classes. Meanwhile, since there are various types of intrusions, classification boundaries between different classes are seriously nonlinear. Due to the huge amount of training data, computational efficiency is also required. This paper therefore proposes an efficient cascaded classifier for NID. This classifier consists of a collection of binary base classifiers which are serially connected. Each base classifier corresponds to a type of intrusion. The order of these base classifiers is automatically determined based on the number of false positives to cope with the unbalance of training samples. Extreme learning machine algorithm, which has low computational cost, is used to train these base classifiers to delineate the nonlinear boundaries between classes. This proposed NID method is evaluated on the KDD99 data set. Experimental results have shown that this proposed method outperforms other state-of-the-art methods including decision tree, back-propagation neural network and support vector machines.

Keywords

Network intrusion detection Cascaded classifier Extreme learning machine 

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yuanlong Yu
    • 1
  • Zhifan Ye
    • 1
  • Xianghan Zheng
    • 1
    Email author
  • Chunming Rong
    • 2
  1. 1.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouChina
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of StavangerStavangerNorway

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