Research article Special Issues

Research on hybrid intrusion detection method based on the ADASYN and ID3 algorithms


  • Received: 30 September 2021 Accepted: 23 November 2021 Published: 27 December 2021
  • Intrusion detection system plays an important role in network security. Early detection of the potential attacks can prevent the further network intrusion from adversaries. To improve the effectiveness of the intrusion detection rate, this paper proposes a hybrid intrusion detection method that utilizes ADASYN (Adaptive Synthetic) and the decision tree based on ID3 algorithm. At first, the intrusion detection dataset is transformed by coding technology and normalized. Subsequently, the ADASYN algorithm is applied to implement oversampling on the training set, and the ID3 algorithm is employed to build a decision tree model. In addition, the model proposed by the research is evaluated by accuracy, precision, recall, and false alarm rate. Besides, a performance comparison is conducted with other models. Consequently, it is found that the combined model based on ADASYN and ID3 decision tree proposed in this research possesses higher accuracy as well as lower false alarm rate, which is more suitable for intrusion detection tasks.

    Citation: Yue Li, Wusheng Xu, Wei Li, Ang Li, Zengjin Liu. Research on hybrid intrusion detection method based on the ADASYN and ID3 algorithms[J]. Mathematical Biosciences and Engineering, 2022, 19(2): 2030-2042. doi: 10.3934/mbe.2022095

    Related Papers:

  • Intrusion detection system plays an important role in network security. Early detection of the potential attacks can prevent the further network intrusion from adversaries. To improve the effectiveness of the intrusion detection rate, this paper proposes a hybrid intrusion detection method that utilizes ADASYN (Adaptive Synthetic) and the decision tree based on ID3 algorithm. At first, the intrusion detection dataset is transformed by coding technology and normalized. Subsequently, the ADASYN algorithm is applied to implement oversampling on the training set, and the ID3 algorithm is employed to build a decision tree model. In addition, the model proposed by the research is evaluated by accuracy, precision, recall, and false alarm rate. Besides, a performance comparison is conducted with other models. Consequently, it is found that the combined model based on ADASYN and ID3 decision tree proposed in this research possesses higher accuracy as well as lower false alarm rate, which is more suitable for intrusion detection tasks.



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