A stacked ensemble learning model for intrusion detection in wireless network

Abstract

Intrusion detection pretended to be a major technique for revealing the attacks and guarantee the security on the network. As the data increases tremendously every year on the Internet, a single algorithm is not sufficient for the network security. Because, deploying a single learning approach may suffer from statistical, computational and representational issues. To eliminate these issues, this paper combines multiple machine learning algorithms called stacked ensemble learning, to detect the attacks in a better manner than conventional learning, where a single algorithm is used to identify the attacks. The stacked ensemble system has been taken the benchmark data set, NSL-KDD, to compare its performance with other popular machine learning algorithms such as ANN, CART, random forest, SVM and other machine learning methods proposed by researchers. The experimental results show that stacked ensemble learning is a proper technique for classifying attacks than other existing methods. And also, the proposed system shows better accuracy compare to other intrusion detection models.

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Correspondence to Usha Devi Gandhi.

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Rajadurai, H., Gandhi, U. A stacked ensemble learning model for intrusion detection in wireless network. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-04986-5

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Keywords

  • Network intrusion detection
  • Gradient boosting
  • Classification algorithms
  • Machine learning
  • Ensemble learning
  • Random forest tree