• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Tri-training algorithm based on cross entropy and K-nearest neighbors for network intrusion detection


Abstract

To address the problem of low detection accuracy due to training noise caused by mislabeling when Tri-training for network intrusion detection (NID), we propose a Tri-training algorithm based on cross entropy and K-nearest neighbors (TCK) for network intrusion detection. The proposed algorithm uses cross-entropy to replace the classification error rate to better identify the difference between the practical and predicted distributions of the model and reduce the prediction bias of mislabeled data to unlabeled data; K-nearest neighbors are used to remove the mislabeled data and reduce the number of mislabeled data. In order to verify the effectiveness of the algorithm proposed in this paper, experiments were conducted on 12 UCI datasets and NSL-KDD network intrusion datasets, and four indexes including accuracy, recall, F-measure and precision were used for comparison. The experimental results revealed that the TCK has superior performance than the conventional Tri-training algorithms and the Tri-training algorithms using only cross-entropy or K-nearest neighbor strategy.


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Cite this article

[IEEE Style]
J. Zhao, S. Li, R. Wu, Y. Zhang, B. Zhang and L. Han, "Tri-training algorithm based on cross entropy and K-nearest neighbors for network intrusion detection," KSII Transactions on Internet and Information Systems, vol. 16, no. 12, pp. 3889-3903, 2022. DOI: 10.3837/tiis.2022.12.006.

[ACM Style]
Jia Zhao, Song Li, Runxiu Wu, Yiying Zhang, Bo Zhang, and Longzhe Han. 2022. Tri-training algorithm based on cross entropy and K-nearest neighbors for network intrusion detection. KSII Transactions on Internet and Information Systems, 16, 12, (2022), 3889-3903. DOI: 10.3837/tiis.2022.12.006.

[BibTeX Style]
@article{tiis:38211, title="Tri-training algorithm based on cross entropy and K-nearest neighbors for network intrusion detection", author="Jia Zhao and Song Li and Runxiu Wu and Yiying Zhang and Bo Zhang and Longzhe Han and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.12.006}, volume={16}, number={12}, year="2022", month={December}, pages={3889-3903}}